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International Journal of Distributed Sensor Networks
Volume 2013 (2013), Article ID 364575, 12 pages
A Profile Based Network Intrusion Detection and Prevention System for Securing Cloud Environment
1Department of Electronics and Computer Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
2Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence (SNIRE), Auburn, WA 98071, USA
3IT4Innovations-Center of Excellence, VSB-Technical University of Ostrava, Ostrava-Poruba 70833, Czech Republic
Received 2 December 2012; Revised 6 February 2013; Accepted 10 February 2013
Academic Editor: Sunilkumar S. Manvi
Copyright © 2013 Sanchika Gupta et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cloud computing provides network based access to computing and data storage services on a pay per usage model. Cloud provides better utilization of resources and hence a reduced service access cost to individuals. Cloud services include software as a service, platform as a service, and infrastructure as a service. Cloud computing virtually and dynamically distributes the computing and data resources to a variety of users, based on their needs, with the use of virtualization technologies. As Cloud computing is a shared facility and is accessed remotely, it is vulnerable to various attacks including host and network based attacks (Brown 2012, and Grance 2009) and hence requires immediate attention. This paper identifies vulnerabilities responsible for well-known network based attacks on cloud and does a critical analysis on the security measures available in cloud environment. This paper focuses on a nonconventional technique for securing cloud network from malicious insiders and outsiders with the use of network profiling. With network profiling, a profile is created for each virtual machine (VM) in cloud that describes network behavior of each cloud user (an assigned VM). The behavior gathered is then used for determination (detection) of network attacks on cloud. The novelty of the approach lies in the early detection of network attacks with robustness and minimum complexity. The proposed technique can be deployed with minimal changes to existing cloud environment. An initial prototype implementation is verified and tested on private cloud with a fully functional implementation under progress.
1. Introduction
Cloud computing according to National Institute of Standards and Technology (NIST) is a service that is provided in the form of computing power and data storage, remotely over internet (network based access) with minimal efforts for resource allocation, management, and release [1]. The US National Institute of Standards and Technology (NIST) has captured five essential cloud characteristics which are [1, 2] (1)on-demand self-service,(2)ubiquitous network access,(3)resource pooling,(4)rapid elasticity,(5)measured service.
Cloud computing provides three major services to its users at various layers of computing. These include [1] the following:(1)software as a service, where a user buys software and application services from cloud service provider in the form of readily and frequently used software such as word processing and data processing software with user having control over its usage but not over the software working and operations,(2)platform as a service, where a user is provided with a platform that can vary from backend supports (for user's own software to run over cloud vendor's space) to provisions for having support for uploading and using third party software on vendor's platform. Here, a user has full control on which software to run and how the software will behave but has dependency on the platform support such as compilers, operating system, and execution frameworks provided by cloud vendor,(3)infrastructure as a service, where a user will have a decent amount of control over the computing infrastructure and storage and he can decide which platform compilers, operating systems, and so forth and software he needs to deploy with full control over them.
With major service providers on the field such as Google, Rackspace, Amazon, and Microsoft cloud computing is one of the most popular forms of computing utilized by users nowadays [3]. The users vary from small companies that wish to use computing and storage infrastructures on a pay per usage model (generally infrastructure and platforms for deploying their own software products) to normal everyday internet users who want to use services such as software, data storage, and platform as a service remotely over internet [3]. The major facility that Cloud computing provides and that makes it different is that it is highly scalable form of computing and can be allocated, reallocated, and released with minimal managerial efforts [1].
The base of Cloud computing lies over virtualization technologies that provide a way to dynamically allocate virtual replicas of physical resources, including memory, processor, and other computing and storage resources. Many of the virtualization software provide facility for creating virtual replicas of physical resources including VMware [4], XEN, and Virtual Box. The virtual replicas provide a way through which users can access the single physical resource simultaneously which eventually increases the resource utilization. As Cloud computing is a remotely accessed form of service and is used by various users, it is vulnerable to attacks ranging from host based attacks to network based attacks and attacks on data storage [5–9].
Cloud environment is vulnerable to a large number of attacks because of the vulnerabilities present in it due to its distributed nature [5, 6]. We have identified vulnerabilities that lead to threats for cloud network environment. The motivation for network attack detection lies in the fact that there is a rapid increase in the number of network based attacks in cloud. We have gathered the statistics of well-known network attacks on cloud that are launched recently. These include the following.(1)The Dropbox (which is a well-known cloud based file sharing facility) getting hacked in July 2012 [10], in this attack, the user personal information which was hacked by attacker from third party websites is used to access Dropbox accounts of users. According to Dropbox, the user name and passwords stolen from other websites about a user are tested on Dropbox accounts. In a large number of the attempts, hackers got success.(2)Attack on Sony PlayStation network which is the second largest attack in US history: this attack took place on May 13, 2011 and has breached online data of Sony PlayStation network [11]. (3)Attack over Amazon EC2 cloud: this attack took place in October 2009 and caused Bit Bucket's servers down for 19 hours [12, 13]. (4)Stratfor (a well-known geopolitical analysis service provider) found its servers breached in the last quarter of 2011 [14].(5)In 2011, an attack on Epsilon (an email marketing company) led to leakage of user credentials such as their names and email addresses through penetrating their customer databases [15].
All such cloud network attacks are a combination of well-known basic network attacks in a specialized manner. We have categorized all such attacks that play as ingredients for specialized cloud network attacks. The well-known and possible network attacks from malicious insiders, users, and outsiders on cloud and the vulnerabilities associated with them are shown in Table 1.
2. Related Work
2.1. The Present Scenario
Various different measures to improve the security of network of either distributed or standalone systems are already known to be researched, proposed, and deployed. Some of the measures include firewalls, network intrusion detection, and prevention systems.
The concept of intrusion detection was known since past and was first proposed by a well-known researcher named Anderson in 1980s [30]. Since the concept was very effective in increasing the security measures of end systems, it gets popularized and now is used and applied in various domains of security.
Since past various intrusion detection and information security approaches for securing Cloud have been proposed and are in practice [27, 31–42], but the concept of network intrusion detection and prevention systems and its deployment strategies over cloud infrastructure, which will be efficient and lightweight, is still going on, because cloud vendors are also interested in a solution that can be deployed and maintained at low cost [5]. Summary of some of the research work carried out in the area of network intrusion detection and prevention in cloud is as follows.
Table 2 provides an abstract description of the work done by the researchers in the area of network intrusion detection in cloud. The table describes the problem areas identified by the researches which are followed by the brief description of the scheme. The table also provides information about the drawbacks of the schemes over cloud with their performance analysis.
2.2. Research Gaps Identified
(1)Existing schemes that do network intrusion detection without VM Profiling are complex as they look for all kinds of attacks on every VM irrespective of the fact that some VMs never launch them. (2)A Normal analysis which includes detection of all well-known attacks from data coming from a large group of VMs on cloud infrastructure is inefficient in the sense that it will waste computing resources for detection of attacks that never happen from a large group of authentic virtual machines.(3)Proposed schemes that deploy IDS at individual virtual machines are vulnerable to host manipulation attacks. Host can subvert the individual IDS or sensors deployed at any virtual machine which will make it difficult to detect network intrusions.(4)Intrusion detection systems which are either signature based or anomaly based are not robust and efficient. This is because signature based detection cannot detect new attacks on the cloud while anomaly based detection systems are not very accurate. Hence, a combination of both is a requirement.(5)There is no concept of ranked detection earlier, that can possibly decrease the amount of time it takes for detecting and responding back to network intrusions in cloud.
The above finding demonstrates the fact that there is an immediate need for a new concept of network intrusion detection for cloud environment that will take the benefits of both signature and anomaly based detection systems with added use of VM profiling that will make it efficient low cost and more responsive in case of frequent and well-known network attacks.
3. The Profile Based Network IDS
Based on the research gaps identified in previous techniques, we have provided a novel architecture for securing cloud towards network based attacks. Our proposed profile based network intrusion detection system is a novel architecture for detecting intrusion from virtual machines (internal entities such as malicious cloud users and insiders) and external entities (outside attackers) on a cloud infrastructure. Our research focuses on providing robust security to cloud network with minimized cost. The cost is in terms of computational power, storage, and communication cost. We have designed and developed a prototype implementation of profile based (anomaly cum signature based) intrusion prevention system for cloud networking infrastructure that will reside in privileged domain and will detect, prevent, and respond to commonly known cloud network attacks.
In our proposed architecture, the total network traffic at privileged domain is filtered based on VM's IP addresses. Network intrusion detection is performed on the packets coming from a particular virtual machine based on its profile. A VM profile describes the attacks that are possible on it for, for example, a VM profile may have entries (thresholds) that dictate that a TCP SYN flooding attack is frequent from it to other VMs on the cloud infrastructure, or it is facing SYN floods from the external world or from other Cloud VMs. This information is gathered by the analysis of signatures of TCP SYN flooding attack in VM network traffic for a period of time. This information is gathered during the initial phase when the VM is assigned to a user and is working in its normal behaviour.
It contains parameters that describe VM's normal incoming and outgoing network traffic behaviour. Hence, whenever packets coming from that VM are collected, they are first analysed for TCP SYN flooding attack, if they are the most frequent attack pattern on that VM. This profile also ranks attacks on the basis of their frequency for, for example, if a particular VM is responsible for TCP SYN flooding attack more frequently than any other attack that is launched from it (in previous history), then it will be analysed first for TCP SYN flooding attack patterns in the traffic than for any other attack pattern. The historical information resides in the profile that ranks the attack patterns and is dynamic in nature. The recently detected attack signature is ranked the highest one in the profile, and the network traffic is detected for attack patterns based on the pattern rank. Hence, the attacks, which are more frequent will be analysed first as they are highly ranked than other signatures in the attack signature db. Also VM profiling helps in developing efficient IDS as VM's traffic is not analysed for all the attacks but only for those attacks which are frequent from them.
This profile of well-known attacks from a virtual machine is created on the basis of anomaly detection component. When a virtual machine is assigned and allocated, the virtual machine traffic is analysed through an anomaly detection component. If an anomaly is detected, the profile of that virtual machine is updated with the attack pattern detected. When an entry is made for that attack in the profile of a virtual machine, the analysis component is informed not to analyse the traffic for known attack patterns on the data coming from that virtual machine for adding them in VM profile as they already exists in the database. But anomaly detection components update the rank of the attack based on the frequency or probability of the attack from VM network behaviour. This anomaly detection component also stores the network characteristic of a particular VM such as bandwidth utilization, packet loss rate, and connection type (secure or not) with signatures of well-known attacks possible or can be launched from that VM as obtained from attack signature db.
Our proposed scheme is deployed at cloud administrator or the privileged cloud service monitor virtual machine which administers and detects attack patterns from other virtual machines allocated to cloud users. Our analysis shows that VM profile based detection increases efficiency and robustness compared to other intrusion detection or prevention architectures. VM profile based network intrusion detection system component collects network packets coming from virtual interfaces of various VMs. As the virtual network interfaces of virtual machines are connected through virtual bridge which privileged domain can access, installing a network packet sniffer on privileged domain is an easiest way to sniff packets for detecting intrusions. Our scheme for detecting intrusions is different from existing network intrusion detection systems as it takes care of virtual machine profile while analysing network packets for intrusion. Virtual machine profile defines what network attacks a virtual machine is vulnerable to with thresholds of the frequent attack patterns detected with their rank.
This helps the intrusion detection component to apply checks only for those intrusions that are possible on a particular virtual machine. VM profile creation, management, and updation will be performed by policy manager component of S-ProIPS. The efficiency is achieved by checking attack patterns on network traffic from a VM only for frequently happening or most probable attacks instead of searching all attack patterns possible. Hence, this will reduce rigorous detection for all attack patterns on the data coming from all VMs even when a large population of VMs are not involved and also are not a victim of such attacks in normal network behaviour. The robustness is achieved as if some specific attack happens over a VM for the first time, it will get detected through the general network security checks applied over data coming from all VMs and newly detected attacks will be added to the VM profile with needed parameters for further detection in future. A common database that contains signature of basic attacks such as packet flooding and which detects abnormal spikes in network traffic always runs in background and contains minimal signatures for pattern matching over all network data which makes this technique a complete one. Our network intrusion detection system is anomaly cum signature based. Whenever a new VM is assigned to a user, its empty profile is created in the VM profile db this is the database for storing VM profile information.
VM profile is a tuple consisting of various features that can be added automatically on the basis of intrusions a particular VM can launch or can suffer in cloud infrastructure. Now, the network packets coming from VMs are analysed for well-known network anomalies, and if an anomaly is detected for a particular VM, its profile is updated with the signature ID of the anomaly detected, which is present in the attack signature db. If such a signature is absent in the attack signature db, a new rule is added in the database. The VM profile is also updated with the parameters needed to detect such intrusions in future. For, for example, a scenario can be as follows: a user (malicious insider or cloud user) does a TCP SYN flooding DDoS attack from a particular VM. The anomaly detection component detects the attack and updates the empty profile of that user with signature ID of TCP SYN flooding attack already present in Attack Signature db. One of the parameters that can be added for such a frequency based attack is threshold. Hence, the VM profile will have a defined value of threshold for detecting a TCP SYN flooding attack on it.
After profile creation, the data (network packets) obtained from that virtual machine is looked for attacks whose signatures are present in VM profile database and match them with attack signature db, and if a match occurs, it sends this information to detection and notification component. After checking for known attacks in a ranked manner, the data from that virtual machine is analysed for anomalies other than signature entries present in attack signature db. Detection and notification component (alert & response) detect the type of attack based on the information received from network intrusion detection system and generate appropriate responses and notifications to handle the attacks on cloud infrastructure. For, for example, responses can be shutting down a particular VM, if it is compromised and is generating DDoS attacks on cloud infrastructure. Notification includes generating and sending alerts to policy manager, so that policies for a particular VM can be updated to prevent such attacks in future. Policy manager (a part of alert & response generation component) can generate new policies and signatures and send them to VM profile db. Our goal is to develop an efficient intrusion detection system in cloud and profile based detection is a way to achieve the same. The design of the proposed architecture is shown in Figure 1. The description of profile based intrusion detection system working can be explained in terms of stages which are as follows.
3.1. Initial Profile Creation Stage
This is the stage which applies when new VMs are allocated to users in cloud. During this stage, the whole traffic from the newly allocated VM is passed through a rigorous checking of attack patterns for a particular time period that can be configured by the administrator. The attack patterns are present in attack signature db. Based on the attack patterns found in the traffic and based on the behavioral analysis of traffic patterns, a profile for a particular VM is created that explains the behavior of the network with probable attacks that it can launch towards other VMs. It also infers the attacks that are possible over that VM. The profile contains the unique identity of attack patterns (referred from attack signature db) that need to be checked on the traffic from that VM during network intrusion detection stage (normal traffic flow path). The description of the steps according to Figure 1 is as follows.(1)In step 1, the network traffic coming from virtual network is gathered at virtual network interface present at the privileged VM.(2)In step 2, the traffic is classified based on the virtual private IP addresses allocated to VM and passed to attack signature db. The attack signature db contains signature of well-known attacks. The VM is analyzed for patterns of attack that it can probably launch or can be vulnerable to in future, based on the behavior of network traffic it has. Like if, from the patterns of attack signature db, it is matched and analyzed that VM-1 generally fires a huge amount of ICMP echo request queries in a small time on cloud VM, there is a probability in future, that the VM can try ping flooding over other cloud VMs. Another example is when traffic of a VM has a huge amount of queries to other systems on certain ports which are used for obtaining system overview than there exists a possibility that cloud user or insider may try such queries over cloud operational systems such as privileged VM and routers to obtain necessary information for launching more severe attacks.
The profile db is updated with the signatures, so that the system will be analyzed for those attack patterns in ranked manner after which general attack patterns are searched for by network intrusion detection system. The traffic is routed to this path of a certain VM for a specified time interval that can be configured for a particular VM to obtain necessary profile information initially. The statistical behavioral analysis of the traffic is also analyzed in this stage and stored in the db that contains percentage of network bandwidth utilization, packet loss ratio, percentage of unencrypted traffic flow, and so forth. The major difference between the common signature detection in background with signatures getting matched through VM profile db is that the matching of attack patterns at VM through VM profile db is fast and more frequent compared to the detection of common signature in background at all traffics. This causes early detection of more frequent attacks and also completes detection of other attacks that are possible but with a saving of resources as the common signature matching is slow as it does rule matching operations after every N packet. It means it searches for attack pattern per N packet to identify intrusion saving resources in terms of pattern matching operations. (3) In step 3, the values obtained by analysis on the network traffic are added to each individual profile of the VMs. (4) The example VM profile db is shown in Table 3 which is specific to DDoS attack detection while Table 4 describes common attack signature db.
3.2. Anomaly Detection Stage
In the anomaly detection stage, the data from the normal traffic path is obtained from the virtual bridge of a VM and is checked consistently for other probable attack pattern behavior. So in this way, robustness is achieved, as after the VM profile creation in the first stage, the VM profile is updated based on the attack patterns as detected in the data coming from a particular VM. The concept lies in ranking the attack patterns in the profile db to improve the time taken for detection as the first attack pattern stored in profile db of a VM is the most probable attack that can be launched to or from other VMs. Also the statistical analysis updates the behavioral measures of network traffic that help in detecting anomalous network behavior over the traffic to and from a particular VM. This helps in detecting intrusions targeted from malicious outsiders towards that VM such as DDoS attacks. The steps are described as follows.(1)In step 4, the data coming from virtual bridge is analyzed for network traffic coming from VMs for new attack patterns from attack signature db. Also the known attack patterns in the db are updated based on the frequency of patterns found in the db, so if their frequency is less, they are ranked less in attack signature db for their detection.
3.3. Network Intrusion Detection Stage
In this stage, the normal traffic flow path from virtual bridge is obtained and analyzed for attack patterns from VM profile db. The traffic of each VM is analyzed for its statistical network behavior and attack pattern from its VM profile db. The anomaly or attack pattern (behavioral anomaly detection and attack pattern signature based detection) found is then reported to the alert and response generation component. The alert and response generation component generates alert and sends it to the administrator for further analysis, while in background it changes the ranking of attack patterns in the VM profile db with their parameters updated for efficient and timely detection of the similar attack for the next time. Various steps in Figure 1 during this stage are described as follows.(1)Step 5 shows the outgoing connection to network intrusion detection component which will divide the flow into individual VM traffic for performing intrusion detection.(2)Step 6 shows the access to VM profile db by the network intrusion detection component. Based on the specific attack patterns and their ranking in VM profile db, the network intrusion detection component checks the network traffic coming from a particular VM for attack patterns. It also checks the anomalous behavioral changes if any after consulting its statistical behavior over a period of time which is stored in VM profile db.(3)Step 7 describes that the output of network intrusion detection component is passed to alert and response generation component if any anomaly or intrusion is detected.(4)In step 8, the response is sent to VM profile db; the response includes increasing the ranking of a particular attack pattern as its occurrence is found and hence is more probable to come in future. Hence, its rank needs to be updated, so that it will be searched relatively earlier to ensure early detection of such frequent attacks.(5)Step 9 shows the outgoing traffic from network intrusion detection component, if it is not containing general attack patterns, specific attack patterns as described by VM profile db and an anomalous traffic behavior.(6)Step 10 shows the incoming traffic received by network intrusion detection component. This traffic based on the VM is addressed and analyzed and then passed to the virtual machine for which it is meant. Any anomalous behavior is passed to alert and response generation component.(7)Step 11 shows the travel of the benign traffic to the virtual bridge which is then transferred to the virtual machine.
4. Results and Discussion
Currently, we have done a prototype implementation of the proposed work in private cloud and have assessed its effectiveness in terms of the network attacks it can prevent. The details of its prototype implementation with advantages are shown in the following subsections.
4.1. Prototype Implementation
The prototype implementation of the proposed work is in progress. We have analyzed the design and started working over it. We are using Perl 5.2.14 as our programming platform for sniffing packets from the virtual network interface of the Cloud network on the privileged domain virtual machine. The private cloud setup is using Open Nebula [43], and individual virtual machines are running variety of OS including Windows 7, 32 bit and Ubuntu 10. The traffic of all the VMs connected virtually through virtual network interface is sniffed using Perl well-known libraries for packet capture including Net: pcap [44] and others. The data of individual VM is analyzed for attack signatures present in attack signature db for prototype implementation; we have taken into consideration DDoS attack scenarios with SYN flooding to provide a proof of concept. The attack signature db contains signature as SYN packets with parameter as maximum number of SYN packets per N packets. This parameter is recorded during VM allocation with normal run. The maximum number of packets per N packets is from a collection of M runs of period of N packets each. The VMs are analyzed for both incoming and outgoing traffic packets for SYN packets. The initial profile for each VM for detecting this attack is created based on the pattern found. However, if during initial profile creation time it crosses some predefined well-defined threshold then immediate actions are taken during initial profile creation time only. In anomaly detection stage, the normal traffic is analyzed for a specified delta in the amount of TCP SYN packets in particular time frame. If the delta increases, the profile is updated with the value.
In the detection stage, the TCP SYN packets are looked over every specified number of packets (N packets) to look over the SYN pattern and its resemblance with the pattern existing in the database for both incoming and outgoing traffic. For the detection of such DDOS attacks, we have taken behavioral and statistical analysis as measure. Hence, we look at the percentage change in the number of SYN packets. If the scenario shows that their percentage is increasing with time we alert the subsystems to inform the admin about the scenario and to take appropriate actions. Even if the DDoS attack is through multiple sources from outside the cloud network, we can stop it as we have the profile of a particular virtual machine known to us, and hence, when a drastic change comes up, such attacks are easily detected and can be resolved by just notifying the VM user of anomalous happenings and shifting him to some different IP address or by blocking the communication from malicious IP addresses. And this will not only detect attacks from malicious outsiders but will also detect malicious insiders that are doing DDoS attacks and can pinpoint the source if inside to cloud environment, as it maintain, the profile of both incoming and outgoing traffics. This is the prototype implementation of the concept; however, the signatures for implementing fully functional prototype are already analyzed, and the implementation for such an intrusion prevention system is under progress. The result will be analyzed in terms of speed of detection of DDoS attacks compared to traditional schemes and the resources required for detection in terms of computational and storage complexity.
4.2. Advantages of Proposed Scheme
The proposed scheme if deployed will provide security to well-known threats over network in cloud both from malicious cloud users, insiders, and outsiders. Table 5 shows the description of cloud network attacks and how they will be handled by the proposed scheme.
5. Conclusions
This paper is focused on an approach of profile based network intrusion detection for providing security to cloud environment. It is a signature cum behavior based network intrusion detection system. The concept is of creating a VM profile db that will describe the attack patterns that needs to be looked over on the VM specific traffic. The VM profile db also contains the attacks in a ranked manner which will ensure timely detection as the patterns will be searched in the traffic in a ranked manner. The profile of a new VM is created in VM profile creation stage where a VM is taken into consideration for a specific amount of time for profiling its behavior for detecting packet flooding attacks such as denial-of-service attacks and also for finding such patterns including others that can be possible in future from that VM or over it, in the cloud network. The profile is updated for new attack patterns and synchronizes attack patterns parameters by anomaly detection component, whereas the network intrusion detection system divides the incoming and outgoing traffics and analyses the network for anomalous behavior for attacks based on the individual profiles of particular VMs. The intrusions are informed to alert and response generation component that then takes necessary actions such as responding to current malicious activities taking place and updating the VM profile with new attack or updating the ranking of attacks for timely detection. The prototype implementation for detecting TCP flooding TCP SYN flooding attack has been done, and the results show, such concept of profile based detection can be applied for detection of all known signature and anomalous behavior based network attacks. The implementation of a complete profile based network intrusion detection system is under progress.
This work was supported by the framework of the IT4 Innovations Centre of Excellence project, reg. no. CZ.1.05/1.1.00/02.0070 by operational programme "Research and Development for Innovations" funded by the Structural Funds of the European Union and state budget of the Czech Republic, EU.
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- L. Flavio and P. Roberto Di, "Secure virtualization for cloud computing," Journal of Network and Computer Applications, vol. 34, no. 4, pp. 1113–1122, 2011. View at Publisher · View at Google Scholar · View at Scopus
- S. Gupta, S. Horrow, and A. Sardana, "IDS based defense for cloud based mobile infrastructure as a service," in Proceedings of the 8th IEEE World Congress on Services (SERVICES), pp. 199–202, Honalulu, Hawaii, USA, 2012.
- A. S. Ibrahim, J. Hamlyn-Harris, J. Grundy, and M. Almorsy, "CloudSec: a security monitoring appliance for Virtual Machines in the IaaS cloud model," in Proceedings of the 5th International Conference on Network and System Security (NSS '11), pp. 113–120, 2011.
- R. Ranchal, B. Bhargava, L. B. Othmane et al., "Protection of identity information in cloud computing without trusted third party," in Proceedings of the 29th IEEE Symposium on Reliable Distributed Systems (SRDS '10), pp. 368–372, November 2010. View at Publisher · View at Google Scholar · View at Scopus
- P. T. J. Arshad and J. Xu, "A novel intrusion severity analysis approach for Clouds," Future Generation Computer Systems, vol. 28, pp. 965–1154, 2011. View at Google Scholar
- S. Pal, S. Khatua, N. Chaki, and S. Sanyal, "A new trusted and collaborative agent based approach for ensuring cloud security," http://arxiv.org/abs/1108.4100.
- O. Nebula, "Open nebula, enterprise cloud and data center virtualization," Open Nebula.org, Open Source Data Center Virtualization, 2013, http://opennebula.org/.
- T. Potter, "Net-Pcap-0.17," CPAN.


text:
Posted by Don on 11/7/2002, 1:01 pm
How is every one
Message Thread
Hello - Don 11/7/2002, 1:01 pm
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subset: commoncrawl


text:
Terry heads the Mobile Library Services department and enjoys each and every one of the communities the bookmobile serves.
This is an official blog for the Marin County Free Library.
All branches of Marin County Free Library will be closed on Monday, July 4 for Independence Day.
All things are possible on opening day! The team is ready, the peanuts (not to mention the garlic fries!) are available, the stats are flying, the "good luck charms" are in place, the fans are ecstatic----PLAY BALL! Now, I'm a long time Giants fan since the 50's----way before that Green and Gold team showed up. (Challenge blog to any of you A's Fans out there!) We froze in Candlestick Park but still cheered our team on to victory! Dia de Los Gigantes----a repeat this year? And of course we've got great baseball books at your library. Check these out!
A note of caution: As we begin the season don't forget to prepare yourself for:
Luckily your local library has lots of self help and meditation books available!


text:
Welcome to the Southwests premiere tennis, squash and racquetball tournament with prize money in excess of $30, 000!
This will be the fourth year of a very successful partnership between Phoenix Motor Company and Camelback Village Racquet and Health Club. The tournament started in 2008 with just tennis, in 2010 squash was introduced and racquetball is being added this year. All tournament events will be played at Camelback Village Racquet and Health Club, 4444 E. Camelback Rd.
Between the three sports, there will be over 500 participants and over the five day period, over 3000 players and spectators will be coming through the doors.
Racquetball will be introduced this year with four 16 player draw: Open, A, B and C as well as an 8 team open doubles for men and open mixed.
Squash will feature 72 players competing in an Olympic format; but new for 2011 will be an Open Invitational division where 8 of the best players from the nation will be invited to play the top 8 players in Arizona.
The tennis component will have over 16 different divisions ranging from 3.5 players all the way to the Open level featuring many national and world ranked players.
Prize money will be paid to winners and division finalists in most of the events draws.
Type Facebook Message below
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text:
Probability and Statistics
Evaluating Probabilities of Boolean Events
The Gambler's Ruin
Integrating the Bell Curve
Poisson Processes and Queues
Ratio Populations
Failure Rates, MTBFs, and All That
Boolean Expansion as Linear Interpolation
At Least One Girl
The Collector's Problem
Periodic and Continuous Repair Models
Collecting k Complete Sets of Coupons
Generalized Birthday Problem (N Items in M Bins)
Independence and Negation
Probabilities With Renaming
Dice Rolling A Given Sum
Redundant Systems With A Common Threat
Meeting Probabilities
A Better Lottery?
Bayesian Marbles
Random High-Low Searching
Hierarchial Repair and Quantum State Reduction
Reliability with Mixed Periodic Repairs
Balls In Bins
Probabilities for Splitting Sets
Choice Without Context?
A Barrel of Colors and Null Outcomes
Open-Loop and Closed-Loop Markov Models
The e in Petersburg
Weibull Analysis
Two Properties of Markov Models
Two Envelopes
Hail Encounters
Dual Failures with General Densities
The Combination of Probabilities
Markov Modeling for Reliability
Prior and Conditional Probabilities
Weibull and Lightning
Reaching Ruin in Limited Turns
Optimizing Your Wife
Complete Markov Models and Reliability
Probabilities and Velocities
Two In the Shop
Differences Between Normal Samples
Benford's Law
Perpendicular Regression Of A Line
On 13 Bridges
Fault Trees and Markov Models
Random Triangles
Infinite Parallel Redundancy
What's the Deal?
N Points On Sphere Fall In One Hemisphere
Bernoulli Trials
Two-Dimensional Runs in Bernoulli Trials
Reliability of Series-Parallel Systems
Sequences of Characters in Random Strings
Botches, Failures, and Successes
Undetected Failures of Threat Protection
A Random Walk Through PI
Mnemonic Coins and Pattern Avoidance
Variations on St. Petersburg
The Shape of Coincidence
Markov Models with Boolean Transition Logic
The Expectation of Posterior Beliefs
Markov Models of Dual-Redundant Systems
Then There Were None
Generating Random Numbers In Order
Biased and Anti-Biased Variance Estimates
Markov's Marbles
Probabilities with Variable Failure Rates
The Y10K Problem, Pascal's Wager, and Petersberg
Expected Area of Random Polygon in a Circle
World's Oldest Man Dies
Asymptotic Rate of an Open Loop Markov Model
Probability of Intersecting Intervals
Timed Markov Models
Never Say Never
Math Pages Main Menu


text:
Carl Quince Armstrong Jr.
Position: C
Height: 6-3 Weight: 230 lbs.
Born: November 22, 1928 in Clyde, TX
High School: R.L. Paschal
College: North Texas (school history)
Drafted by the New York Giants in the 26th round (314th overall) of the 1951 NFL Draft.
Weighted Career AV (100-95-...): 0 (15060th overall since 1950)


text:
Joran Vliegen
May 13, 2014
GREENVILLE, N.C. --- The accolades continued to roll in for East Carolina senior Joran Vliegen Tuesday as he was named the Intercollegiate Tennis Association (ITA) Carolina Region Most Improved Senior-of-the-Year, the organization announced.
The 2014 Conference USA Player-of-the-Year, Vliegen posted an overall singles record of 31-7, which included a 16-4 dual match record at the No. 1 position during the spring pushing him over 100 career singles wins. He has a career record of 108-29 entering next week's NCAA Division I Men's Tennis Singles Championships in Athens, Ga.
The native Belgian is currently No. 25 in the ITA singles rankings. He received his first career ranking in the final 2013 ratings at No. 125 and entered the 2013-14 season at No. 87 before peaking at No. 13 in February. Vliegen posted an 8-4 record against ranked opponents this season and won the 2013 ITA Carolina Region singles title.
He paired with sophomore Colin Roller throughout the season for a 20-6 overall doubles record with a 14-4 mark at the No. 1 spot during dual match play. The duo reached the round of 16 at the ITA All-American Championships and was ranked as high as 30th during the year. They became the first players in school history to participate in the NCAA Doubles Championships last season.
The 2014 NCAA Division I Men's Singles Championships will begin May 21 on the campus of the University of Georgia.


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Should you cut the (cable) cord?
Lately I have heard of many Facebook friends "cutting the cord" & going over to "ROKU" "NETFLIX" "HULU" "APPLE TV", etc. There are significant financial savings to this, BUT there are also disadvantages. For many of these to work you MUST have "Internet". Unless you have gone the "Old School" path of the antenna (oh the days of free tv!). Many are combining and still pay no where near what their "Triple Play Bills" were costing them.
BEFORE you schedule your shut off ask yourself (and honestly answer) a few questions:
- How valuable is TV to you?: If you look at your Cable/Satellite bill each and every month and swear one of these days you will downgrade your package, or cut it off entirely (only if you knew of an better alternative) .then you're ready to "cut the cable". There is an alternative for you. Ones I believe will make you and your wallet very happy. If you watch A LOT of television shows, go to social media when your fave show is on to discuss every part of it with like minded people, countdown until a premiere or finale or special episode, then I believe "cutting the cord" is not for you. Right now anyways. Of course there are always circumstances where our preferences take a back seat. This post and the links in it will help anyone considering "cutting the cord".
- (A big "THANK YOU" to Beth W. for suggesting I include this as question #1. Do not forget to click on her name to go to her blog & check out it's awesomeness.)
- How do I want to watch TV?: Switching often requires a good, strong, (not wifi from a neighbor) broadband connection and an device strong enough to stream. If you have a laptop, tablet, iPod, WIFI Enabled TV,etc. ask yourself: "Am I comfortable watching ALL of my TV shows and movies on this device forever?" "Do I have more than one device in case someone else wants to be online for a different reason?" "Can I afford to replace and/or fix a device that konks out? (happens A LOT more than to a TV & Cable will come out and replace/fix their products for free)"
- When do I want to watch my favorite shows?: You cannot watch the breaking news on Netflix, Hulu, Apple TV, etc. Nor will you be able to keep up with that "must see" show ("The Walking Dead", Series finale of "The Breaking Bad", "The Vampire Diaries", *insert your fave show here*). A lot of times you can find said episode the next day if you have the right app, or site to go to, or do not mind watching a "may not be legal, but I am not sure" episode download online. IF you do not mind avoiding ALL forms of social media after a big episode, avoiding others who adore the same show(s) as you until you see the episode, then you'll honestly be a-ok.
- What do I like to watch?: Make a list of your fave shows. Are they on network (ABC,NBC,The CW) or Cable (A&E, FX, USA)? If you can't live without "NBC's" "The Voice" or "The Big Bang Theory" then you may be able to get by with an antenna. Rabbit Ears are as little as 8$. An outdoor antenna can run you 50$. This could be bad news depending upon your address. Check out this site: ANTENNA WEB.ORG input your address and zip code. Here you will get a very accurate idea of signal strength in your area. Cable show fanatic? Some shows stream a lot, others such as "Homeland" are not available quickly & they will cost you a few bucks via Itunes or similar sites. Die hard Sports Fans? Sorry my friend, you WILL have to stick with Cable/Satellite.
- The Cost?: Add up all of the monthly streaming services you will use (Netflix, HULU, etc.), cost of any shows/seasons you may buy (Homeland fans need apply), Are you a Redbox type of person? Add those 1.25$ per movie costs in there too. Do not forget equipment. IF the cost is LESS than your typical Cable/Satellite bill, and you CAN live with the lifestyle change, then by all means go for it. Remember you will need to add in cost of your Internet too.
How to Watch (non Cable) TV Now:
For descriptions, and prices, of the most popular streaming, and downloading, services, go to:
(This link has been a wealth of information. It breaks down EACH of the popular streaming sites, the cost, what it includes, etc. A "must have book marked link" for those considering the switch or adding to the package they already have.)
Bella Asks:
Are you ready to drop your Cable/Satellite company? Why or why not? If yes, what might you miss the most, the least? Was your decision based on more than a financial one? Any suggestions for others considering the same?
Bella Thanks: "REAL SIMPLE"
Related articles
Bella's Shelf
Latest posts by Bella's Shelf (see all)
- Homemakers by Brit Morin - March 11, 2015
- Trial by Fire by Josephine Angelini - March 3, 2015
- Plus One by Elizabeth Fama - March 2, 2015
- Weighing on my Mind.. - February 26, 2015


text:
Gardening Articles: Care :: Pests & Problems
Corn Diseases, Insects, and Pests (page 2 of 4)
by National Gardening Association Editors
These are the insects most likely to affect corn, along with the best control measures for the home gardener.
Corn earworm is also known as the tomato fruitworm or cotton bollworm. This 1- to 2-inch-long caterpillar ranges from light green to purplish brown. Moths lay eggs on corn plants in early summer and larvae feed first on the silks, then on the kernels at the tip of each ear. The insect can prevent pollination, and it opens kernels to fungus invasion. To discourage this pest, select varieties with tightly closed husks. Earworms can be controlled somewhat by squirting mineral oil into each ear after silks have started to dry, using half a medicine dropper per ear. You can also spray the plant and silks with Bt. If earworm damage occurs, clip off the tip of the ear and any affected kernels. The rest of the ear should be fine to eat.
European corn borer is a 1-inch-long tan or brown caterpillar sporting rows of dark brown spots and a dark brown head. The moths fly mostly at night and lay eggs on the undersides of corn leaves in early summer. The hatched larvae bore into cornstalks and ears to feed. Broken tassels, bent stalks and "sawdust" around corn are all signs of borer damage. If you catch it in time, you can often cut out the borer from the stalk with no permanent damage. To prevent infestation, treat ears and leaves with Bt as soon as silk has partially emerged. Spray with an organic pesticide at five-day intervals from the time you first spot borer activity or when the tassels begin appearing. Make at least two applications for best results. Be sure to till or spade under crop residues at the end of the season so the borer has no place to overwinter.
Corn Sap Beetles are small, black beetles that spawn maggot-like larvae that eat into the kernels of roasting corn. Larvae are whitish and up to 1/4 inch long. The beetles are attracted to the scent of damaged corn, so preventing feeding by other pests helps keep them at bay. To prevent, plant resistant varieties and clean up all crop residues. Southern corn rootworms are small, yellowish grubs of the spotted cucumber beetle. They weaken corn plants by feeding on roots, causing the stalks to blow over easily in wind or heavy rain. Adults lay eggs around roots of cornstalks in the fall. The eggs hatch in spring. You can avoid damage from corn rootworms by tilling under cornstalks and rotating crops each year.
Corn Root Aphids are tiny, light green insects that feed on corn roots, causing the plants to be stunted and yellowed. The aphids overwinter in the nests of cornfield ants. The best way to control this pest is to plow the garden in the fall, destroying ant nests. Corn Flea Beetles are small but dangerous. Only 1/16th of an inch long, these jumping black beetles chew corn foliage and transmit Stewart's bacterial wilt. The pests abound during cool, wet periods and after mild winters. They hibernate in weeds and plant debris over the winter, so keep the garden and surrounding areas clean. Many of the later-maturing white corn varieties are resistant to wilt. To repel flea beetles, sprinkle a light dusting of wood ash over plants and soil.
Wireworms are slender, yellowish or brown larvae of click beetles. These 1/2- to 1 1/2-inch-long worms resemble a jointed wire. They damage corn plants by feeding on the roots. These pests are most often present in newly worked sod. Rotate crops and till or spade your garden thoroughly in the fall. Heavy infestations may require soil treatment with beneficial nematodes before planting.
Seed corn maggots are cream-colored and legless. The 1/2-inch-long larvae bore into sprouting seeds and prevent further growth. When early corn is planted in cool, wet soil, the slower germination makes the seeds more susceptible to maggot attack. If maggots are a problem in your area, delay planting until weather warms.


text:
This project was begun in 2008 and completed in 2012 as collaboration between the Chester Fritz Library, the UND Center for Human Rights and Genocide Studies, and the UND Department of English. The documents on this site were digitized by undergraduate and graduate students in various English 417 and English 428 courses under the supervision of Dr. Crystal Alberts.
The materials were transcribed by members of the digitization team and then verified to ensure accuracy. When a word or letter was unclear, it has been marked as [illegible] or placed in brackets. With the exception of removing hyphens noting a break in a word over a line, the text appears as it does in the original. Page images have been also been made available.
To ensure the persistence and preservation of this project, it is maintained by the University of North Dakota Chester Fritz Library.
Portions of this project were made possible through the generous support of the Norges Hjemmefrontmuseum
Dr. Crystal Alberts, Brianna Berg, Brock Berglund, Rebekah Billings, Meghan Brown, Erin Carr, Alex Cavanaugh, Sally Cook, Laura Cory, Holly Edwards, John Fitzgerald, Nick Gowan, Lucy Hartmann, Douglas Haugen, Andrea Herbst, Debrah Iverson, Betsy Jensen, Brandon Jordan, Brooke LaDouceur, Whitney Ledger, Kari Mitchell, Stephanie Myers, Kaylee Nesdahl, Abbey Numedahl, Jennifer Pierce, Michael Raatz, Jennifer Steele, Mary Stromme, Katy Thiel, Ericka Erickson, Brad Rose, Devon Olson, Lindsey Humfeld, Jordan Thiery, Nathan Guillemette, Phil Brockman, and Jamie Hutchinson.


text:
Ricky C. Williams
Position: RB
Height: 6-1 Weight: 195 lbs.
Born: April 27, 1960 in Santa Monica, CA
High School: Santa Monica
College: Langston
Drafted by the New England Patriots in the 9th round (233rd overall) of the 1983 NFL Draft.
Weighted Career AV (100-95-...): 0 (15060th overall since 1950)
Our primary support comes from users like you. Sponsor this page for $5. Your message will replace this ad.


text:
Info & Directions
Fabulous Menu
The Back Door Bar & Grille is located at 1250 Bardstown Road, adjacent to the Mid City Mall in the heart of the picturesque Highlands neighborhood in Louisville, KY. For driving directions, please try Mapquest.
As a reviewer in LEO noted, the Back Door is "one of Louisville's favorite after-work, meet–my-friends, meet-strangers, game-of-pool, bag-of-wings, one-more-stop-before-home watering holes."
The Back Door is owned and operated by John Dant, who purchased the building from Tom and Linda Clemons in 1987. The 5,000-square-foot bar and lounge previously served as a warehousing and storage facility for the nearby A&P Grocery. The patio area was a loading and unloading dock for the store. Our establishment is open seven days a week from 4 p.m. until 4 a.m., and is strictly a "21 and over" bar. ID's are checked.
Our bar staff includes Bar Manager Carrie, Dave, Marianne, Cindy, and Chris. The kitchen staff includes Chef Kat. We take great pride in the fact that Carrie, Dave, Cindy and Marianne have been with The Back Door for over 20 years.
The owners and
employees take great pride in the Back Door's reputation of being one of
the friendliest bars in Louisville. Long-time customers, newcomers and
out-of-town visitors can relax and mingle in a casual and cordial
With 10 high definition television monitors, the Back Door is a favorite of local fans of the University of Louisville and University of Kentucky.
A spacious arcade room includes some of the most popular contemporary games, including Golden Tee Video Golf, Foosball, Darts and other favorites, as well as ample seating. Other offerings include counter video games in the main bar area.
The Back Door
has a separate
room with high quality
pool tables that are carefully maintained
well lighted. A total of 6 tables are available for individual
play, competition or for special
parties. We also
operate the Metro Pool League. Competition is held on Monday evenings,
starting at


text:
Walmart (WMT) is the world's biggest retail stock, but that doesn't guarantee stability.
Walmart earnings could be rough yet again this week, and WMT could take a tumble as a result.
Walmart earnings in May were disappointing, featuring a same-store sales slip for the first time in almost two years. Those who owned Walmart stock at any time from 2009 to 2011 should remember the unfortunate drag on shares caused by nine consecutive quarters of same-store sales declines during that stretch.
Consider that from April 2009 to September 2011, Walmart stock literally went nowhere while the S&P 500 added more than 50%.
Granted, it hasn't been an easy road for any retailer. Old-guard department stores JCPenney (JCP) and Sears (SHLD) have struggled mightily to adapt to the new economic environment and to fend off e-commerce from Amazon (AMZN). At the same time, weak consumer spending has been an issue for all kinds of retailers over the last several years, from teen clothing giant Abercrombie (ANF) to electronics retailer Best Buy (BBY) to upscale big-box store Target (TGT) to every store in between.
Now, WMT was full of excuses on why earnings disappointed a few months ago. Walmart blamed cold, wet weather holding back sales of seasonal items and blamed a delay on income tax returns that normally boosts spring revenue. But Walmart earnings this week won't have the luxury of similar excuses to defuse investors' fears, and there's a lot riding on these numbers.
Walmart earnings have been characterized by poor top-line growth amid weak consumer spending at home; the unfortunate reality of having a low-price appeal means that your customer base is made up of the poorest consumers who are most susceptible to pullbacks in discretionary spending.
Coupled with heavy spending to enter overseas markets that haven't must begin paying off, Walmart earnings haven't had much upside. WMT stock has only slightly underperformed in 2013 with 14% returns year-to-date, but the stock's roughly 57% returns from the March 2009 lows are less than half the gains of the broader stock market in the same period.
After setting a high near $80 in right before Walmart's first-quarter earnings showed a miss on sales, the stock hasn't made much of a move. That's telling, and proof that Walmart stock investors are in wait-and-see mode.
With just a 2.4% dividend yield, folks are waiting for this next round of Walmart earnings to prove either that the stock has stabilized or to confirm that the troubles of last quarter are persistent.
If we see another Walmart earnings miss this week, expect another long stretch of underperformance for WMT.
Related Reading
- Walmart's sales go cold in May. (NYT)
- Walmart stock is one of three dividend plays to dump now. (MarketWatch)
- WMT considers buying a supermarket chain for $6 billion. (InvestorPlace.com)
Jeff Reeves is the editor of InvestorPlace.com and the author of The Frugal Investor's Guide to Finding Great Stocks. Write him at email@example.com or follow him on Twitter via @JeffReevesIP. As of this writing, he did not own a position in any of the stocks named here.


text:
"Eaglet Admitted to Wildlife Center of Virginia"
The Wildlife Center has provided the following initial information regarding their care of the Norfolk Botanical Garden eaglet. As more information becomes available we will pass it long.
"The baby Bald Eagle arrived at the Wildlife Center at about 7 p.m. on Thursday. The bird was assigned a patient number – 08-887 – and given a preliminary physical examination by Drs. Dave McRuer and Mark Ruder.
The eagle weighed in at one kilogram and was in good body condition. Of concern is a soft tissue mass on the left side of the upper beak. The eagle was given fluids and antibiotics and was fed three mice – which it ate vigorously.
Later today the Wildlife Center veterinary staff will take radiographs of the eagle. The Center is also working to arrange a CT scan of the eagle's head at another medical-care facility...
...The eaglet is now being housed in a fake nest in a small enclosure. The eaglet will be fed a mixture of cut-up mice and fish two to three times daily."


text:
When I'm a fan of a product, I generally shout it from the rooftops. I mean, spread the wealth, right? Well, I'm on my rooftop: listen up! A coworker of mine introduced me to Better Oats, and I'm fairly sure that these days, I'm made mostly of whole grain oats and flax seeds. No, really. Really.
Ease: dudes, it doesn't get any easier than this. All you need is a bowl, spoon, and a microwave. Once you pour the oats into your bowl, you get to reuse the handy-dandy pouch as a measuring device for your water. Sweet! (And virtually dish-free.) 90 seconds later? Voila!
Health: most packets are between 100 – 160 calories. I'm a serious sugar-holic, so trust me when I say that you actually don't need to add a thing to these! Also, they are really filling! Opposed to those bins-o-oats (which of course have a time and place… especially for No-Bake Cookies… [see, told you I was a sugar-holic, as well as very easily distracted]); Better Oats have all you need stuffed in each little packet of goodness. They're super yummy for breakfast or lunch. Or dinner. Even my 2-year-old shovels down her own little package on a near-daily basis.
Go to the Better Oats website for more info- including a coupon to try them out!
(PS- The only store I haven't been able to find these at is at WalMart, otherwise, I've found them at a plethora of other stores. And I usually buy them like
10 3 boxes at a time…)


text:
BERKELEY, Calif. ?- For the first time all year, the Arizona women's basketball team had five players in double-digit scoring and handed California its first loss at Haas Pavilion today, defeating the Bears 80-76. With the win, the Wildcats improve to 7-12 overall and 3-6 in Pac-10 play.
Arizona shot 44 percent from the field, 50 percent from three-point range and 76 percent from the free throw line on the game. California shot 49 percent from the field, but just 14 percent from beyond the arc and 35 percent from the charity stripe on the game. The Bears dominated the boards 48 to 34.
"This is a great win for us, for our confidence," Head Coach Joan Bonvicini said. "We've obviously had a lot of adversity this season. It would have been easy to lay down and make excuses, but we didn't. I'm really proud of this team."
Junior Joy Hollingsworth led all players with a season-high 23 points. She also added a team-high eight rebounds to go with six assists and four steals. Sophomore Ashley Whisonant added 15 points, while sophomore Jessica Arnold finished with a season-high 14 points. Senior Natalie Jones and freshman Kelsey Burns totaled 10 points apiece. Burns' total was a career high. Whisonant dished out a team-high seven assists.
After California scored the first basket of the game, Arizona went on a 7-0 run to take a 7-2 lead. A Hollingsworth three-pointer later gave the Wildcats a six-point lead. At the 12:18 mark, Bonvicini was given a technical foul and shortly later the Bears closed the gap, making it a one-point game, 16-15. With 9:03 left, Cal took its first lead (21-19) since the opening moments of the game. The Bears later extended their lead to nine points (32-23) and went into the locker room at halftime up 38-29.
Arizona shot 32 percent from the field, 33 percent from three-point range and 80 percent from the free throw line on the half. California connected on 51 percent from the field but just 20 percent from the charity stripe and failed to make a three-pointer (0-for-7). The Bears out-rebounded the Wildcats 27 to 18. Both teams committed eight turnovers on the half. Whisonant and Hollingsworth each had nine points on the half.
Cal took its biggest lead of the game (40-29) at the start of the second half, but a pair of Arnold treys made it a five-point game. A 6-0 Wildcat run later cut the Bear lead to three, and shortly later, a Jones long ball gave Arizona its first lead (46-45) since the 10:02 mark of the first half.
The Wildcats then came alive from downtown hitting three treys in a span of 1:08 to take an eight-point lead, 57-49. Arizona later led by as many as 10, but the Bears chipped away at it, making it a four-point game (63-59) with 7:54 left. The Wildcats responded with a 9-0 run to take their biggest lead of the game, 72-59.
California then went on a run of its own, scoring eight straight to make it a five-point game, 74-69. The Bears later cut it to two (75-73) with 28 seconds left, but an Anna Chappell bucket with 18 left put Arizona back up by four, 77-73. The Bears scored just their second three-pointer of the game with 12 seconds left in the game to make it a one-point game.
Whisonant was then fouled with seven seconds remaining and made both. An intentional foul by Arnold with 4.2 seconds remaining sent Cal's Alexis Gray-Lawson to the line. She missed both, sealing the Wildcat win.
Arizona plays at Washington at 7 p.m. (PST) next Thursday, Jan. 26.


text:
According to Comic Book Movie on Monday, in an interview with the "Captain America: The Winter Soldier's" Russo brothers directors in regards to a promotion for the upcoming Blu-ray movie there was a matter of thoughts on Hawkeye's Clint Barton even being in "Captain America 2." Co-director Joe Russo mentioned that if he was in the second movie, it would have changed things because earlier plans for the sequel would have involved the artisan archer. It was more of a scheduling issue according to the co-director.
In fact, it would have entailed an additional 10 to 15 minutes of running time to the film as it would need to be carefully and methodically planned out. Such a time would have been a requisite to making Hawkeye part of the film if he were to have been included in it. There had been a couple of rather "cool scenes" according to the co-director.
"He had a couple of really cool scenes in the movie. It's a big movie as it is, so it certainly helped that it's not in there. I think it would increase the running time by about 10 or 15 minutes if they were in the movie, just because you want the right amount of time to actually treat those scenes properly. I think it was a scheduling issue."
Another issue that was addressed was whether or not ABC's "Marvel's Agents of S.H.I.E.L.D." would also be impacted by the events that will occur in "Captain America 3", Russo stated that there's no way to know at this time as it's an ever evolving process. However, since the events in "Captain America: The Winter Soldier" left S.H.I.E.L.D. abandoned and now currently out of the equation, it's likely no longer a huge component that would tie into "Captain America 3." There is no longer a grand scale situation as the agents have gone rogue.
"Captain America 3" opens in theaters come May 6, 2016.
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In Wake Of Loss, UMass Tennis Continues To Raise Funds For Cancer Research
June 29, 2012
Maria Francis battled cancer for eight years before succumbing to the disease on June 10, 2012. Her daughter, Gianna, is a rising sophomore on the UMass women's tennis team - a group which became more than just a support group for her, but for her mother and all who battle the disease as well.
Part of that support came in the way of partnering with Stand Up To Cancer which is a national organization that raises money for cancer research. The group partners with scientist and doctors focusing on collaborative research in an effort to bring treatments that will directly benefit patients in need.
For its part, the UMass tennis team started Team Maria Francis as part of the SUTC network. The Minutewomen have been able to raise $1,800 towards research thanks to supporters who purchase a $5 wristband.
Along with the team page on the SUTC website, the team also started a facebook page to help promote the cause through social media.
Gianna's family and the members of the UMass tennis team have asked that fans continue to support Team Maria in the fight against cancer.
For more information or to purchase a wristband, visit www.StandUp2Cancer.org or visit the team's facebook page at http://www.facebook.com/pages/StandUp2Cancer-Project-Maria-Francis/158305024285034.
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Home / LSA Research / Funding / LSA Funding Sources /
The Ralph Baldwin Prize in Astrophysics and Space Science
The Ralph B. Baldwin Prize in Astrophysics and Space Sciences is an award sponsored by a generous gift to the University by Dr. Ralph B. Baldwin. Applicants must show original and significant contributions to their field as measured in their scholarly publications. (Click here for a list of past winners.)
Student applications are selected from the fields of Astronomy, Atmospheric, Oceanic, and Space Science; Chemistry; Physics; Geology; and Mathematics as they pertain to space, its bodies, and relevant physical processes. The prize is awarded annually to a student who has received a University of Michigan Ph.D. during the previous year. A faculty committee comprised of representatives from the appropriate departments judges the packages submitted on the basis of the excellence of their research activities revealed in the student's thesis and publications during their career.
Each department nominates theses completed in the previous calendar year. Last year's runner up might be considered. Each department submits to the College up to two outstanding applications for final selection.
Applications should consist of 1 electronic copy of the following e-mailed to email@example.com:
- Student's thesis
- Candidate's curriculum vitae
- Two letters of recommendation
- Publication List
- Statement of research from candidate
- Candidate's graduate transcripts
Deadlines and Timeline
The deadline for the applications to the College is April 4, 2016.
Award Amount
This award acknowledges the contribution of outstanding students in their department with a grant of $3,500 and a $500 travel stipend. The award is presented to the students at a College ceremony.


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I'm asked to prove that the Cantor set has no intervals. This is what I've got:
Let $(a,b)\subseteq [0,1]$, where $a<b$. Let $T=\{-\log_3(b-a)<n:n\in\mathbb{N}\}$. Now if $(a,b)\subseteq[0,1]$, then $b-a<1$, and so $-\log_3(b-a)>0$. This would then mean that $-\log_3(b-a)\in\mathbb{R}$, and so by the Archimidean property there exists some $\alpha\in T$ such that $a\leq \beta$ for all $\beta\in T$. Hence $$-\log_3(b-a)<\alpha,$$ so we have know that $$3^{-(-\log_3(b-a))}>3^{-\alpha}.$$ Of course the LHS here reduces the inequality as $$b-a=\lvert b-a \rvert < 3^{-\alpha}.$$ Now $A_{\alpha}$ is the union of subsets of $[0,1]$ having length $3^{-\alpha}$, so $(a,b)\not\subseteq A_{\alpha}$; hence $(a,b)\not\subseteq C$, as was to be shown.
I'm wondering what your opinion on the proof is. Good? Bad? Incorrect? Throw it away! There's a faster way! You know, all of this.


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College Hockey:No. 13 Vermont Edges Colgate, 6-4
BURLINGTON, Vt. — After a nearly three week long holiday break, No. 13 Vermont returned to the ice Friday night and held on to defeat Colgate, 6-4, in the opening round of the 13th Catamount Cup.
Sporting their jet black alternate jerseys in front of a sold-out crowd of 4,003 at Gutterson Fieldhouse, Vermont rang in the new year with four power-play goals and survived a late comeback by the Raiders to improve to 10-4-2 (6-3-1 in Hockey East).
Senior assistant captain Peter Lenes and sophomore Justin Milo led the offensive onslaught with two goals apiece while junior goaltender Mike Spillane finished with 14 saves. The non-conference loss drops Colgate to 5-8-4 (1-4-3 in the ECAC).
Overall I was really pleased with our effort, said Vermont coach Kevin Sneddon. I thought for most of the game we looked like we were in our game-shape and executed some plays very, very nicely today on offense, did some really good things defensively for most of the game. It was the first time in a while that we actually fed off our penalty kill.
Despite being outshot 12-3 in the opening period, Colgate nearly took a 1-0 lead less than seven minutes in. With Vermont senior assistant captain Corey Carlson already in the sin bin for hitting-from-behind, junior linemate Viktor Stalberg was called for slashing at 6:10 to give the Raiders an abbreviated 5 on 3 power play. Almost cashing in, Colgate captain Nick St. Pierre unleashed a blistering slapshot from the left point that clanked off the goal post to the right of Spillane. Vermont went on to kill off the remainder of Stalbergs penalty.
Taking supreme advantage of Colgates missed opportunities, Vermont stormed back on the power play midway through the period to go up 1-0. Set up in the right corner, junior Brian Roloff found Milo on the doorstep and the opportunistic center hammered the puck past Raiders goaltender Charles Long for his seventh tally of the season.
Gaining momentum as the opening period winded down, Vermont extended their lead to 2-0 at 14:13. Parked at the left face-off dot, junior winger Colin Vock found Lenes streaking in from the blue line. Displaying a calculated sense of urgency, Lenes fired a one-timer just before being buried by Colgate defenseman Mark Anderson. The shot deflected off a maze of skaters in front and slid by a helpless Long. The even-strength snipe was Lenes sixth of the year.
Vermont continued to surge and secured a 3-0 lead on the power play less than two minutes later. Entering the Colgate zone with speed, Stalberg waited for Raiders defenseman David Sloane to commit to the shot and then dished the puck to Roloff near the left dot. Uncovered, Roloff unleashed a sizzling wrister past Long for his fifth strike of the season.
Attempting to energize his idling club, Colgate coach Don Vaughan pulled Long in favor of freshman Alex Evin to start the second period. Nonetheless, Vermont continued to dominate and extended the lead to 4-0 at 9:10. Intercepting an attempted clear, Vock found Lenes all alone in front. The bottle rocket winger promptly deked Evin and then lifted a backhand past the netminder high, glove side.
Vermonts seemingly commanding lead shrunk to 4-1 just over a minute later when Spillane mishandled a clearing attempt near the corner boards. Colgates Austin Smith jumped on the loose puck and fired it into the empty net for his ninth goal of the season.
Just 30 seconds later Vermont responded on the power play to make it 5-1. Quarterbacking the man advantage, Milo fired a shot on net from the left point that deflected by Evin for his second tally of the game.
After a Wahsontiio Stacey power-play goal at 1:34 of the third period put Vermont on top 6-1, Colgate began a spirited comeback and scored three unanswered goals in less than three minutes to make it 6-4. Forward David McIntyre potted the first two, deflecting a feed from Anderson past Spillane at 4:16 and then beating the Vermont goaltender again with a wrister from the hashmarks just 42 seconds later.
Smith assisted on both tallies, the seventh and eighth of the season for McIntyre. Raiders forward Jason Williams lit the lamp on the power play at 6:55 to end the rally.
Credit Colgate with their play in the third period, said Sneddon. Sometimes its tough when youve got such a big lead- they play like they had nothing to lose. We kind of tightened up a little bit and didnt take care of business defensively.
Despite two more opportunities with the man advantage, including one in the final two minutes that allowed Vaughan to pull Evin for an extra attacker which produced an abbreviated 6-on-4 power play, Colgate failed to inch any closer before the final buzzer sounded.
Vermont capitalized on most of their opportunities and you have to give them credit, said Vaughan. I thought they were more ready to play than we were. You cant expect to come out against a team like that with a lack of energy that early in the game and expect to [win]. Youre down 6-1 to a team like this in this building, its a tall order.
Vermont senior captain Dean Strong finished with one assist, leaving him four points shy of 100 for his career. Roloff ended up with a goal and two assists.
Winning the special teams battle, Vermont showed little rust after a 19 day layoff and went 4-8 on the power play while holding Colgate to just 1-5.
You never know what to expect after a long break, said Milo. I think we had a good week of practice, really battled hard, really focused on what we needed to do instead of what the other team is going to bring us. I think that really carried over into this game.
Vermont looks to avenge a Dec. 13 4-3 loss to St. Lawrence when they host the Saints Saturday night in the championship game of the Catamount Cup.
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Elijah Farmen's three interceptions help Medical Lake roll over Rams 42-6 for first 4-0 start in a decade
For access to this article please sign in or subscribe.
By PAUL DELANEY
Staff Reporter
After racing to a big victory the week before over Newport in the start of Northeast A League play, the Medical Lake Cardinals showed up last Friday for their Holliday Field game against Riverside with their offensive engine sputtering and in need of a tune-up.
"We just weren't quite firing on all cylinders that first quarter," head coach Wes Hobbs said.
But sophomore linebacker Elijah Farmen's second of an eventual three interceptions seemed to fix...


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Anthony Vincent Mumford
Position: RB
Height: 6-0 Weight: 215 lbs.
Born: June 14, 1963 in Philadelphia, PA
High School: Overbrook Regional
College: Penn St. (school history) (Mumford college stats)
Drafted by the New England Patriots in the 12th round (328th overall) of the 1985 NFL Draft.
Weighted Career AV (100-95-...): 0 (15060th overall since 1950)
Other SR Links: College Football at Sports-Reference.com


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Can excessive pumping of the California Central Valley's groundwater be setting up the Sacramento regional area and the rest of the Central Valley or Northern California for an increasing number of earthquakes? If you're worried about increasing earthquakes in Northern or Southern California, or the Sacramento regional area, one possible cause points to depletion of groundwater due to the three-year drought added to excessive pumping of existing groundwater.
The heavy weight of the groundwater used to hold down the plates from slipping and causing frequent earthquakes. But now that the groundwater has been pumped so frequently, not much weight is left holding down the plates from slipping, says recent research from the National Science Foundation. The scientists report their results online in the May 14, 2014 issue of the journal Nature. You may wish to check out, "Uplift and seismicity driven by groundwater depletion in central California." There's also related audio online: Colin Amos speaks in that podcast on how a loss of water in California's Central Valley may be moving mountains. Or see recent news also published online in Nature, "Earth science: Fertile fields for seismicity."
What that amounts to is that there's much California's Central Valley Winter rains and summer groundwater pumping in California's Central Valley. The result, says recent research is that the excessive groundwater pumping makes the Sierra Nevada and Coast Mountain Ranges sink and rise by a few millimeters each year, creating stress on the state's faults that could increase the risk of an earthquake.
That's what recent research from the National Science Foundation explains. Gradual depletion of the Central Valley aquifer, because of groundwater pumping, also raises these mountain ranges by a similar amount each year--about the thickness of a dime--with a cumulative rise over the past 150 years of up to 15 centimeters (6 inches), according to calculations by a team of geophysicists.
While the seasonal changes in the Central Valley aquifer have not yet been firmly associated with any earthquakes, studies have shown that similar levels of periodic stress, such as that caused by the motions of the moon and sun, increase the number of microquakes on the San Andreas Fault
If these subtle seasonal load changes are capable of influencing the occurrence of microquakes, it's possible that they can sometimes also trigger a larger event, said Roland Bürgmann, a geoscientist at the University of California, Berkeley and co-author of the paper appearing May 14, 2014 online in the journal Nature.
"The stress is very small, much less than you need to build up stress on a fault leading to an earthquake, but in some circumstances such small stress changes can be the straw that breaks the camel's back," Bürgmann said, according to the May 14, 2014 news release, California Central Valley groundwater depletion slowly raises Sierra Nevada mountains. "It could just give that extra push to get a fault to fail."
The study, based on GPS measurements from California and Nevada between 2007 and 2010, was led by scientists Colin Amos at Western Washington University and Pascal Audet of the University of Ottawa
The detailed GPS analyses were performed by William Hammond and Geoffrey Blewitt of the University of Nevada, Reno, as part of a National Science Foundation (NSF) grant. Hammond and Blewitt, along with Amos and Audet, are also co-authors of this week's paper. "Other studies have shown that the San Andreas Fault is sensitive to small-scale changes in stress," said Amos, according to the news release. "These appear to control the timing of small earthquakes on portions of the fault, leading to more small earthquakes during drier periods of the year. Previously, such changes were thought to be driven by rainfall and other hydrologic causes."
This work ties overuse of groundwater by humans in the San Joaquin Valley to increases in the height of nearby mountain ranges and possible increases in the number of earthquakes on the San Andreas Fault, said Maggie Benoit, according to the news release. Benoit is program director in NSF's Division of Earth Sciences, which funded the research.
"When humans deplete groundwater," said Benoit, according to the news release, "the amount of mass or material in Earth's crust is reduced. That disrupts Earth's force balances, causing uplift of nearby mountains and reducing a force that helps keep the San Andreas fault from slipping."
Draining of the Central Valley
Water has been pumped from California's Central Valley for more than 150 years, changing what used to be a marsh and extensive lake, Tulare Lake, into fertile agricultural fields. In that time, about 160 cubic kilometers (40 cubic miles) of water was removed--the capacity of Lake Tahoe--dropping the water table in some areas more than 120 meters (400 feet) and the ground surface 5 meters (16 feet) or more.
The weight of water removed allowed the underlying crust or lithosphere to rise by so-called isostatic rebound, which may have raised the Sierra as much as half a foot since about 1860. The same rebound happens as a result of the state's seasonal rains.
Torrential winter storms drop water and snow across the state, which eventually flow into Central Valley streams, reservoirs and underground aquifers, pushing down the crust and lowering the Sierra 1-3 millimeters. In the summer, water flow into the Pacific Ocean, evaporation and ground water pumping for irrigation, which has accelerated because of drought, allows the crust and surrounding mountains to rise again.
Bürgmann said, according to the news release, that the flexing of Earth's crust downward in winter would clamp the San Andreas fault tighter, lowering the risk of quakes, while in summer the upward flexure would relieve this clamping and perhaps increase the risk. "The hazard is ever so slightly higher in the summer than in the wintertime," he said, according to the news release. "This suggests that climate and tectonics interact, and that water changes ultimately affect the deeper Earth."
High-resolution mapping with continuous GPS
Millimeter-precision measurements of elevation have been possible only in the last few years. Improved continuous GPS networks--part of the NSF EarthScope Plate Boundary Observatory, which operates 1,100 stations around the western United States--and satellite-based interferometric synthetic aperture radar have provided the data.
The measurements revealed a steady yearly rise of the Sierra of 1-2 millimeters per year, which was initially ascribed to tectonic activity deep underground, even though the rate was unusually high. The new study provides an alternative and more reasonable explanation for the rise of the Sierra in historic times.
"The Coast Range is doing the same thing as the Sierra Nevada, which is part of the evidence that this can't be explained by tectonics," Bürgmann said in the news release. "Both ranges have uplifted over the last few years and both exhibit the same seasonal up and down movement in phase. This tells us that something has to be driving the system at a seasonal and long-term sense, and that has to be groundwater recharging and depletion."
In response to the current drought, about 30 cubic kilometers (7.5 cubic miles) of water has been removed from Central Valley aquifers between 2003 and 2010, causing a rise of about 10 millimeters (2/5 inch) in the Sierra over that time. For further information, you may wish to check out the NSF Grant: Revealing the Nature of Contemporary Uplift and Collapse in the Sierra Nevada - Great Basin System (II). Another noteworthy program is the NSF EarthScope Program. Or take a look at the National Science Foundation's (NFS') Earthscope Plate Boundary Observatory. The Earthscope Plate Boundary Observatory (PBO) component of the EarthScope precisely measures Earth deformation resulting from the constant motion of the Pacific and North American tectonic plates in the western United States.
The National Science Foundation (NSF) is an independent federal agency that supports fundamental research and education across all fields of science and engineering. In fiscal year (FY) 2014, its budget is $7.2 billion. NSF funds reach all 50 states through grants to nearly 2,000 colleges, universities and other institutions.
Each year, NSF receives about 50,000 competitive requests for funding, and makes about 11,500 new funding awards. NSF also awards about $593 million in professional and service contracts yearly. You may wish to see the NSF Home Page. You also may wish to look at the site, Science and Engineering Statistics. Or check out the Awards Searches website.


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It's never a good sign when the owner is calling the shots. Owner Jerry Jones, the self appointed general manager said Tuesday that Bill Callahan will be calling the plays for the Dallas Cowboys offense next season.
Coach Jason Garrett had been calling the plays, but since the season ended on a sour note, Jones moved in to shake things up. Along with the firing of defensive coordinator Rob Ryan, this was a major blow to Garrett, and it sets up a showdown that could quickly become a parting of ways.
According to Calvin Watkins of ESPNDallas.com, Jones stated that Garrett wouldn't be the offensive play caller any longer, and that offensive coordinator/line coach Bill Callahan would take over the duty.
Callahan, before working for the Cowboys was the offensive line coach for the New York Jets, but he didn't call plays there. However, Callahan called plays as a head coach for the Oakland Raiders in the 2002 and 2003 seasons.
The bigger twist is if this is enough to be the straw that breaks the camel's back. Garrett has had his staff ravaged and replaced by Jones, including targeting Garrett's brother John, who left to take a job with the Tampa Bay Buccaneers, before he was to be fired.
The school of thought is that Jones is trying to force Garrett out without looking like a firing, preferring an amicable parting of ways.


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Lewis C. Holder
Position: E
Height: 6-0 Weight: 191 lbs.
Born: October 10, 1923 in Dallas, TX
High School: Woodrow Wilson
College: Texas (school history)
Weighted Career AV (100-95-...): 0 (15060th overall since 1950)
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Output includes all games from 1920 to 2015.


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I had to install windows 7 on my macbook air (...) and I'm having trouble getting it to work with the thunderbolt display.
As soon as the display is connected, the wifi doesn't work anymore. Device manager shows the broadcom wifi device with a warning sign. It works nicely when I reboot without the display plugged in.
Of course I've installed the newest Bootcamp drivers!
Is this a known problem? Any suggestions?
(If it helps, I'm also having trouble getting my wireless trackpad and wireless keyboard to work at all with windows 7, might be related..)
(un)fortunately I don't know jack about windows anymore because I didn't use it seriously for at least 10 years.