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8, November 2010
An Anomaly-Based Network Intrusion Detection System Using Fuzzy Logic
R. Shanmugavadivu
Assistant professor, Department of Computer Science PSG College of Arts & Science, Coimbatore-14 email@example.com
Principal, Coimbatore Institute of Engineering and Information Technology, Coimbatore. email@example.com
Abstract—IDS which are increasingly a key part of system defense are used to identify abnormal activities in a computer system. In general, the traditional intrusion detection relies on the extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, various data-mining and machine learning techniques have been used in the literature. In the proposed system, we have designed fuzzy logic-based system for effectively identifying the intrusion activities within a network. The proposed fuzzy logic-based system can be able to detect an intrusion behavior of the networks since the rule base contains a better set of rules. Here, we have used automated strategy for generation of fuzzy rules, which are obtained from the definite rules using frequent items. The experiments and evaluations of the proposed intrusion detection system are performed with the KDD Cup 99 intrusion detection dataset. The experimental results clearly show that the proposed system achieved higher precision in identifying whether the records are normal or attack one. Keywords-Intrusion Detection System (IDS); Anomaly based intrusion detection; Fuzzy logic; Rule learning; KDD Cup 99 dataset.
or behavior that is regarded as abnormal by a pre-defined criterion [8]. Intrusion detection has emerged as a significant field of research, because it is not theoretically possible to set up a system with no vulnerabilities [9]. One main confrontation in intrusion detection is that we have to find out the concealed attacks from a large quantity of routine communication activities [10]. Several machine learning (ML) algorithms, for instance Neural Network [11], Support Vector Machine [12], Genetic Algorithm [13], Fuzzy Logic [14], and Data Mining [15] and more have been extensively employed to detect intrusion activities both known and unknown from large quantity of complex and dynamic datasets. Generating rules is vital for IDSs to differentiate standard behaviors from strange behavior by examining the dataset which is a list of tasks created by the operating system that are registered into a file in historical sorted order [16]. Various researches with data mining as the chief constituent has been carried to find out newly encountered intrusions [17]. The analysis of data to determine relationships and discover concealed patterns of data which otherwise would go unobserved is known as data mining. Many researchers have used data mining to focus into the subject of database intrusion detection in databases [18]. According to the detection strategy used, data miningbased intrusion detection systems can be classified into two main categories [23]. They are misuse detection which identifies intrusions using patterns of well known intrusions or weak spots of the system and anomaly detection, which attempts to find out if departure from the recognized standard usage patterns can be flagged as attacks [19]. (a) Misuse Detection: On the basis of the impressions of known intrusions and known system weaknesses misuse detection tries to model abnormal activities. (b) Anomaly Detection: Both user and system behavior can be predicted using normal behavior patterns. Anomaly detectors identify possible attack attempts by constructing profiles representing normal usage and then comparing it with current behavior data to find out a likely mismatch [20]. For specified, well-known intrusion excellent detection results are achieved by signature-based methods. But, they cannot find out unfamiliar intrusions though constructed as a least alteration of previously known attacks. Conversely, the capability of discovering intrusion
I. INTRODUCTION Intrusion incidents to computer systems are increasing because of the commercialization of the Internet and local networks [1]. Computer systems are turning out to be more and more susceptible to attack, due to its extended network connectivity. The usual objective of the aforesaid attacks is to undermine the conventional security processes on the systems and perform actions in excess of the attacker's permissions. These actions could encompass reading secure or confidential data or just doing vicious destruction to the system or user files [2]. A system security operator can detect possibly malicious behaviors as they take place by setting up intricate tools, which incessantly monitors and informs activities [22]. Intrusion detection systems are turning out to be progressively significant in maintaining adequate network protection [1, 3, 4, 5]. An intrusion detection system (IDS) watches networked devices and searches for anomalous or malicious behaviors in the patterns of activity in the audit stream [6]. Capability of discriminating between standard and anomalous user behaviors should be present in a good intrusion detection system [7]. This would comprise of any event, state, content,
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events which are previously unobserved is the main advantage of anomaly-based detection techniques [21]. In the proposed system, we have designed anomaly based intrusion detection using fuzzy logic. The input to the proposed system is KDD Cup 1999 dataset, which is divided into two subsets such as, training dataset and testing dataset. At first, the training dataset is classified into five subsets so that, four types of attacks (DoS (Denial of Service), R2L (Remote to Local), U2R (User to Root), Probe) and normal data are separated. After that, we simply mine the 1-length frequent items from attack data as well as normal data. These mined frequent items are used to find the important attributes of the input dataset and the identified effective attributes are used to generate a set of definite and indefinite rules using deviation method. Then, we generate fuzzy rule in accordance with the definite rule by fuzzifying it in such a way, we obtain a set of fuzzy if-then rules with consequent parts that represent whether it is a normal data or an abnormal data. These rules are given to the fuzzy rule base to effectively learn the fuzzy system. In the testing phase, the test data is matched with fuzzy rules to detect whether the test data is an abnormal data or a normal data. The rest of the paper is organized as follows: Section II presents literature review of the proposed system and section III describes the detailed analysis of the KDD cup 99 dataset. The proposed intrusion detection system using fuzzy logic is given in section IV. Experimentation and performance analysis of the proposed system is discussed in section V. Finally, the conclusion is given in section VI. II. REVIEW OF RECENT RESEARCH Several techniques are available in the literature for detecting the intrusion behavior. In recent times, intrusion detection has received a lot of interest among the researchers since it is widely applied for preserving the security within a network. Here, we present some of the techniques used for intrusion detection. S. F. Owens and R. R. Levary [24] have stated that intruder detection systems have been commonly constructed using expert system technology. But, Intrusion Detection System (IDS) researchers have been biased in constructing systems that are difficult to handle, lack insightful user interfaces and are inconvenient to use in real-life circumstances. The proposed adaptive expert system has utilized fuzzy sets to find out attacks. The expert system comparatively easy to implement when used with computer system networks has the capability of adjusting to the nature and/or degree of the threat. Experiments with Clips 6.10 have been used to prove the adjusting capability of the system. Alok Sharma et al. [25] have focused on the use of text processing techniques on the system call sequences for intrusion detection. Host-based intrusions have been detected by introducing a kernel based similarity measure. Processes have been classified either as normal or abnormal using the knearest neighbor (kNN) classifier. They have assessed the proposed method on the DARPA-1998 database and compared its operation with other existing methods present in the literature.
Shi-Jinn Horng et al. [26] have used a combination of hierarchical clustering algorithm, easy feature selection method, and SVM technique in their proposed SVM-based intrusion detection system. Fewer, abstracted, and higherqualified training instances that are derived from the KDD Cup 1999 training set has been given to the SVM by the hierarchical clustering algorithm. The simple feature selection method employed for the removal of insignificant features from the training set has enabled the proposed SVM model to achieve more precise classification of the network traffic data. The proposed system has been assessed using the renowned KDD Cup 1999 dataset. Compared to other intrusion detection systems that are based on the same dataset, the proposed method has exhibited superior performance in identifying DoS and Probe attacks and an overall best performance in accuracy. B. Shanmugam and Norbik Bashah Idris [28] have proposed an advanced fuzzy and data mining methods based hybrid model to find out both misuse and anomaly attacks. Their objective was to decrease the quantity of data kept for processing and also to improve the detection rate of the existing IDS using attribute selection process and data mining technique respectively. A modified version of APRIORI algorithm which is an improved Kuok fuzzy data mining algorithm utilized for implementing fuzzy rules has enabled the generation of if-then rules that show common ways of expressing security attacks. They have achieved faster decision making using mamdani inference mechanism with three variable inputs in the fuzzy inference engine which they have employed. The DARPA 1999 data set has been used to test and benchmark the efficiency of the proposed model. In addition, the test results against the "live" networking environment within the campus have been analyzed. O. A. Adebayo et al. [29] have presented a method that uses Fuzzy-Bayesian to detect real-time network anomaly attack for discovering malicious activity against computer network. They have established the effectiveness of the method by describing the framework. The overall performance of the intrusion detection system (IDS) based on Bayes has been improved by a combination of fuzzy with Bayesian classifier. In addition, by the experiment carried out on KDD 1999 IDS data set, the practicability of the method has been verified. Abadeh, M.S. and Habibi, J. [27] have proposed a method to develop fuzzy classification rules for intrusion detection use in computer networks. The method of fuzzy rule base system design has been based on the iterative rule learning approach (IRL). Using the evolutionary algorithm to optimize one fuzzy classifier rule at a time, the fuzzy rule base has been created in an incremental fashion. Intrusion detection problem has been used as a high-dimensional classification problem to analyze the functioning of the final fuzzy classification system. Results have demonstrated that the fuzzy rules generated by the proposed algorithm can be utilized to build a reliable intrusion detection system. Arman Tajbakhsh et al. [30] have presented a data mining technique based framework for constructing an IDS. In the framework, Association Based Classification (ABC) has been used by the classification engine which is in fact the central part of the IDS. Fuzzy association rules have been used by the proposed classification to construct classifiers. Some matching
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measures have been used to evaluate the consistency of any new sample (which is to be categorized) with various class rulesets and the label of the sample has been declared as the class that is analogous to the best matched ruleset. A method which decreases the items that may be included in extracted rules has also been proposed to reduce the time taken by the rule induction algorithm. The framework has been assessed using KDD-99 dataset. The results have shown that the achieved total detection rate and detection rate of known attacks are large and false positive rate is small, though the results are not bright for unknown attacks. Zhenwei Yu et al. [31] have presented an automatically tuning intrusion detection system (ATIDS). According to the feedback supplied by the system operator, when false predictions are detected, the proposed system automatically tunes the detection model on-the-fly. The KDDCup'99 intrusion detection dataset has been used to assess the system. In the experimental results, the system has demonstrated a 35% enhancement with regard to misclassification cost compared to a system that is not using the tuning feature. If the model has been tuned using only 10% false predictions still a 30% improvement is achieved by the system. Moreover, the model tuned using only 1.3% of the false predictions have been capable of achieving about 20% improvement provided the tuning is not delayed too long. Building a practical system based on ATIDS has been proved to be feasible by the results of the experiments: Because predictions ascertained to be false have been used for tuning the detection model, system operators can concentrate on confirmation of predictions with low confidence. III. KDD CUP 99 DATASET In 1998, DARPA in concert with Lincoln Laboratory at MIT launched the DARPA 1998 dataset for evaluating IDS [36]. The DARPA 1998 dataset contains seven weeks of training and also two weeks of testing data. In total, there are 38 attacks in training data as well as in testing data. The refined version of DARPA dataset which contains only network data (i.e. Tcpdump data) is termed as KDD dataset [37]. The Third International Knowledge Discovery and Data Mining Tools Competition were held in colligation with KDD99, the Fifth International Conference on Knowledge Discovery and Data Mining. KDD dataset is a dataset employed for this Third International Knowledge Discovery and Data Mining Tools Competition. KDD training dataset consists of relatively 4,900,000 single connection vectors where each single connection vectors consists of 41 features and is marked as either normal or an attack, with exactly one particular attack type [38]. These features had all forms of
continuous and symbolic with extensively varying ranges falling in four categories: • In a connection, the first category consists of the intrinsic features which comprises of the fundamental features of each individual TCP connections. Some of the features for each individual TCP connections are duration of the connection, the type of the protocol (TCP, UDP, etc.) and network service (http, telnet, etc.). • The content features suggested by domain knowledge are used to assess the payload of the original TCP packets, such as the number of failed login attempts. • Within a connection, the same host features observe the recognized connections that have the same destination host as present connection in past two seconds and the statistics related to the protocol behavior, service, etc are estimated. • The similar same service features scrutinize the connections that have the same service as the current connection in past two seconds. A variety of attacks incorporated in the dataset fall into following four major categories: Denial of Service Attacks: A denial of service attack is an attack where the attacker constructs some computing or memory resource fully occupied or unavailable to manage legitimate requirements, or reject legitimate users right to use a machine. User to Root Attacks: User to Root exploits are a category of exploits where the attacker initiate by accessing a normal user account on the system (possibly achieved by tracking down the passwords, a dictionary attack, or social engineering) and take advantage of some susceptibility to achieve root access to the system. Remote to User Attacks: A Remote to User attack takes place when an attacker who has the capability to send packets to a machine over a network but does not have an account on that machine, makes use of some vulnerability to achieve local access as a user of that machine. Probes: Probing is a category of attacks where an attacker examines a network to collect information or discover well-known vulnerabilities. These network investigations are reasonably valuable for an attacker who is staging an attack in future. An attacker who has a record, of which machines and services are accessible on a given network, can make use of this information to look for fragile points. Table I illustrates a number of attacks falling into four major categories and table II presents a complete listing of a set of features characterized for the connection records.
Denial of Service Attacks User to Root Attacks Remote to Local Attacks Probes
Back, land, neptune, pod, smurf, teardrop Buffer_overflow, loadmodule, perl, rootkit, Ftp_write, guess_passwd, imap, multihop, phf, spy, warezclient, warezmaster Satan, ipsweep, nmap, portsweep
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Feature index 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
Feature name duration protocol_type service flag src_bytes dst_bytes Land wrong_fragment urgent hot num_failed_logins logged_in num_compromised root_shell su_attempted num_root num_file_creations num_shells num_access_files num_outbound_cmds is_hot_login is_guest_login count srv_count serror_rate srv_serror_rate rerror_rate srv_rerror_rate same_srv_rate diff_srv_rate srv_diff_host_rate dst_host_count dst_host_srv_count dst_host_same_srv_rat e dst_host_diff_srv_rate dst_host_same_src_po rt_rate dst_host_srv_diff_host _rate dst_host_serror_rate dst_host_srv_serror_ra te dst_host_rerror_rate dst_host_srv_rerror_ra te
Description length (number of seconds) of the connection type of the protocol, e.g. tcp, udp, etc. network service on the destination, e.g., http, telnet, etc. normal or error status of the connection number of data bytes from source to destination number of data bytes from destination to source 1 if connection is from/to the same host/port; 0 otherwise number of ``wrong'' fragments number of urgent packets number of ``hot'' indicators number of failed login attempts 1 if successfully logged in; 0 otherwise number of ``compromised'' conditions 1 if root shell is obtained; 0 otherwise 1 if ``su root'' command attempted; 0 otherwise number of ``root'' accesses number of file creation operations number of shell prompts number of operations on access control files number of outbound commands in an ftp session 1 if the login belongs to the ``hot'' list; 0 otherwise 1 if the login is a ``guest'' login; 0 otherwise number of connections to the same host as the current connection in the past two seconds number of connections to the same service as the current connection in the past two seconds % of connections that have ``SYN'' errors % of connections that have ``SYN'' errors % of connections that have ``REJ'' errors % of connections that have ``REJ'' errors % of connections to the same service % of connections to different services % of connections to different hosts count for destination host srv_count for destination host same_srv_rate for destination host diff_srv_rate for destination host same_src_port_rate for destination host diff_host_rate for destination host serror_rate for destination host srv_serror_rate for destination host rerror_rate for destination host srv_serror_rate for destination host
Type continuous symbolic symbolic symbolic continuous continuous symbolic continuous Continuous Continuous Continuous Symbolic Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Symbolic Symbolic continuous Continuous continuous Continuous Continuous Continuous Continuous Continuous Continuous continuous continuous continuous continuous continuous continuous continuous continuous continuous continuous
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IV. AN ANOMALY-BASED NETWORK INTRUSION DETECTION SYSTEM USING FUZZY LOGIC Presently, it is unfeasible for several computer systems to affirm security to network intrusions with computers increasingly getting connected to public accessible networks (e.g., the Internet). In view of the fact that there is no ideal solution to avoid intrusions from event, it is very significant to detect them at the initial moment of happening and take necessary actions for reducing the likely damage [32]. One approach to handle suspicious behaviors inside a network is an intrusion detection system (IDS). For intrusion detection, a wide variety of techniques have been applied specifically, data mining techniques, artificial intelligence technique and soft computing techniques. Most of the data mining techniques like association rule mining, clustering and classification have been applied on intrusion detection, where classification and pattern mining is an important technique. Similar way, AI techniques such as decision trees, neural networks and fuzzy logic are applied for detecting suspicious activities in a network, in which fuzzy based system provides significant advantages over other AI techniques. Recently, several researchers focused on fuzzy rule learning for effective intrusion detection using data mining techniques. By taking into consideration these motivational thoughts, we have developed a fuzzy rule based system in detecting the attacks. This system, anomaly-based intrusion detection makes use of effective rules identified in accordance with the designed strategy, which is obtained by mining the data effectively. The fuzzy rules generated from the proposed strategy can be able to provide better classification rate in detecting the intrusion behavior. Even though signature-based systems provide good detection results for specified and familiar attacks, the foremost advantage of anomaly-based detection techniques is their ability to detect formerly unseen and unfamiliar intrusion occurrences. On the other hand and in spite of the expected erroneousness in recognized signature specifications, the rate of false positives in anomaly-based systems is generally higher than in signature based ones [21]. The different steps involved in the proposed system for anomaly-based intrusion detection (shown in figure 1) are described as follows: (1) Classification of training data (2) Strategy for generation of fuzzy rules (3) Fuzzy decision module (4) Finding an appropriate classification for a test input
Figure 1. The overall steps of the proposed intrusion detection system
(1) Classification of Training Data The first component of the proposed system is of classifying the input data into multiple classes by taking in mind the different attacks involved in the intrusion detection dataset. The dataset we have taken for analyzing the intrusion detection behavior using the proposed system is KDD-Cup 1999 data. The detailed analysis of KDD-Cup 1999 data is given in section III. Based on the analysis, the KDD-Cup 1999 data contains four types of attacks and normal behavior data with 41 attributes that have both continuous and symbolic attributes. The proposed system is designed only for the continuous attributes because the major attributes in KDDCup 1999 data are continuous in nature. Therefore, we have taken only the continuous attributes for instance, 34 attributes from the input dataset by removing discrete attributes. Then, the dataset ( D ) is divided into five subsets of classes based on the class label prescribed in the dataset D = {Di ; 1 ≤ i ≤ 5} . The class label describes several attacks, which comes under four major attacks (Denial of Service, Remote to Local, U2R and Probe) along with normal data. The five subsets of data are then used for generating a better set of fuzzy rules automatically so that the fuzzy system can learn the rules effectively. (2) Strategy For Generation of Fuzzy Rules This section describes the designed strategy for automatic generation of fuzzy rules to provide effective learning. In general, the fuzzy rules given to the fuzzy system is done manually or by experts, who are given the rules by analyzing intrusion behavior. But, in our case, it is very difficult to generate fuzzy rules manually due to the fact that the input data is huge and also having more attributes. But, a few of researches are available in the literature for automatically
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identifying of fuzzy rules in recent times. Motivated by this fact, we make use of mining methods to identify a better set of rules. Here, definite rules obtained from the single length frequent items are used to provide the proper learning of fuzzy system. The process of fuzzy generation is given in the following sub-section. (a) Mining of single length frequent items At first, frequent items (attributes) are discovered from both classes of input data and by using these frequent items, the significant attributes are identified for the input KDD-cup 99 dataset. In general, frequent itemset are mined using various conventional mining algorithms, such as Apriori [35] and FP-Growth [40]. These algorithms are suitable to mine frequent itemset with varying length only for the binary database, which contains only the binary values. But, the input dataset (KDD cup-99) contains continuous variable for each attributes so that, the conventional algorithm is not suitable for mining frequent items. By considering this property, we simply find the 1-length items from each attributes by finding the frequency of the continuous variable present in each attribute and then, the frequent items are discovered by inputting the minimum support. These frequent items are identified for both class namely, normal and attack (combining four types of attacks). (b) Identification of suitable attributes for rule generation In this step, we have chosen only the most suitable attributes for identifying the classification whether the record is normal or attack. The reason behind this step is that the input data contain 34 attribute, in which all the attributes are not so effective in detecting the intrusion detection. For identifying the suitable attribute, we have used deviation method, where mined 1-length frequent items are used. At first, the mined l-length items from each attribute are stored in a vector so that 34 vectors are obtained for each class (class 1 and class 2), represented as, Ci = V1 , V2 , L , V j , L , V34
attributes in between the normal and attack data, the intersection points are identified for the effective attributes. By making use of these two intersection points, the definite and indefinite rules are generated. For example, {max, min} deviation range for normal data related to attribute1 is {1, 5} and {max, min} deviation for attack data corresponding to attribute1 is {2, 8}. Then, the rule is designed like, "IF attribute1 is greater than 5, THEN the data is attack, "IF attribute1 is in between 2 and 5, THEN the data is normal OR attack" and "IF attribute1 is less than 2, THEN the data is normal". In addition to that, some of the data contains only one intersection point, which provides only two rules. (d) Rule filtering In order to learn the fuzzy rules efficiently and design a compact and interpretable classification system, we should concentrate in these two criteria given in [33, 34]: (1) The number of fuzzy rules should be decreased as much as possible, (2) The IF part of fuzzy rules should be short. By concentrating on these two criteria, we have filtered the rules such a way that, we take only the short and less number of rules. The rules that are generated from the previous step contain definite and indefinite rules. The definite rules are the rules that contain only one classified label in the THEN part and indefinite rule contain two classification label data in the THEN part. The proposed rule filtering technique filters the indefinite rule and selects only the definite rules for learning the fuzzy system. (e) Generating fuzzy rules In general, fuzzy rules are defined within the fuzzy system manually or the rules are obtained from the domain expert. But, in the proposed system, we automatically find the fuzzy rules based on the mined 1-length frequent items. The fuzzy rules are generated from the definite rules, where the IF part of the rule is a numerical variable and THEN part is a class label related to attack name or normal. But, the fuzzy rule should contain only the linguistic variable. So, in order to make the fuzzy rules from the definite rules, we should fuzzify the numerical variable of the definite rules and THEN part of the fuzzy rule is same as the consequent part of the definite rules. For example, "IF attribute1 is H, THEN the data is attack and "IF attribute1 is VL, THEN the data is normal". These fuzzy rules are used to learn the fuzzy system so that the effectiveness of the proposed system will be improved rather than simply using the fuzzy rules without any proper techniques. (3) Fuzzy Decision Module This section describes the designing of fuzzy logic system for finding the suitable class label of the test dataset. Zadeh in the late 1960s [39] introduced Fuzzy logic and is known as the rediscovery of multivalued logic designed by Lukasiewicz. The designed fuzzy system shown in figure 2 contains 34 inputs and one output, where inputs are related to the 34 attributes and output is related to the class label (attack data or normal data). Here, thirty four-input, single-output of Mamdani fuzzy inference system with centroid of area defuzzification strategy was used for this purpose. Here, each input fuzzy set defined in the fuzzy system includes four membership functions (VL, L, M and H) and an output fuzzy set contains two membership functions (L and H). Each
where, i = 1 (refer to normal) , 2 (refer to attack) . Each vector ( V j ) contains frequent items, whose frequency is greater than minimum support.
V j = { f i ; 1 ≤ i ≤ m} ;
support(f i ) ≥ min_supp . Then, for each attribute, deviation range of frequent items is identified by comparing the frequent items present within a vector such a way, the deviation range {max, min} is obtained for every vector.
Dv( j ) = { f max , f min } ; where, f max = Max( f j ) ;
f min = Min f j
( )
Then, one-to-one comparison is performed in between both class of respective vector to identify the effective attribute. The attributes that not contain identical {max, min} range for both class is chosen as effective attribute, which will give significant detection rate rather than utilizing the all attribute for identifying the classification. The effective attributes chosen for rule generation process is represented as, C i = V (1) , V ( 2) , L , V ( j ) , L , V ( k ) , Where, k ≤ 34 . (c) Rule generation The effective attributes chosen from the previous step is utilized to generate rules that is derived from the {max, min} deviation. By comparing the deviation range of effective
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membership function used triangular function for fuzzification strategy. The fuzzy rules obtained from sub-section IV.B are fed to the fuzzy rule base for learning the system.
Testing Dataset Normal 26,000 DOS 26,000 Probe 4107 RLA 77 URA 42 A. Experimental Results and Performance Analysis The training dataset contains normal data as well as four types of attacks, which are given to the proposed system for identifying the suitable attributes. The selected attribute for rule generation process is given in table V. Then, using the fuzzy rule learning strategy, the system generates definite and indefinite rules and finally, fuzzy rules are generated from the definite rules.
Figure 2. The designed Fuzzy system
(4) Finding an Appropriate Classification for a Test Input For testing phase, a test data from the KDD-cup 99 dataset is given to the designed fuzzy logic system discussed in subsection IV.C for finding the fuzzy score. At first, the test input data containing 34 attributes is applied to fuzzifier, which converts 34 attributes (numerical variable) into linguistic variable using the triangular membership function. The output of the fuzzifier is fed to the inference engine which in turn compares that particular input with the rule base. Rule base is a knowledge base which contains a set of rules obtained from the definite rules. The output of inference engine is one of the linguistic values from the following set {Low and High} and then, it is converted by the defuzzifier as crisp values. The crisp value obtained from the fuzzy inference engine is varied in between 0 to 2, where '0' denotes that the data is completely normal and '1' specifies the completely attacked data. V. EXPERIMENTATION This section describes the experimental results and performance evaluation of the proposed system. The proposed system is implemented in MATLAB (7.8) and the performance of the system is evaluated using Precision, recall and F-measure. For experimental evaluation, we have taken KDD cup 99 dataset [37], which is mostly used for evaluating the performance of the intrusion detection system. For evaluating the performance, it is very difficult to execute the proposed system on the KDD cup 99 dataset since it is a large scale. Here, we have used subset of 10% of KDD Cup 99 dataset for training and testing. The number of records taken for testing and training phase is given in table III and table IV.
Attribute Index 1 5 6 8 9 10 11 13 16 17 18 19 23 24
Selected Attributes duration src_bytes dst_bytes wrong_fragment urgent hot num_failed_logins num_compromised num_root num_file_creations num_shells num_access_files count srv_count
In the testing phase, the testing dataset is given to the proposed system, which classifies the input as a normal or attack. The obtained result is then used to compute overall accuracy of the proposed system. The overall accuracy of the proposed system is computed based on the definitions, namely precision, recall and F-measure which are normally used to estimate the rare class prediction. It is advantageous to accomplish a high recall devoid of loss of precision. Fmeasure is a weighted harmonic mean which evaluates the trade-off between them.
Precision =
Training Dataset Normal 25,000 DOS 25,000 Probe 4107 RLA 77 URA 42
TP TP + FP TP Recall = TP + FN
F − measure = ( β 2 + 1)( Precision ⋅ Recall )
β 2 ⋅ Precision + Recall
where, β = 1
Overall accuracy =
Where, TP TN
True positive True negative
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FN False negative FP False positive These are computed using the confusion matrix in Table VI, and defined as follows:
VI. CONCLUSION We have developed an anomaly based intrusion detection system in detecting the intrusion behavior within a network. A fuzzy decision-making module was designed to build the system more accurate for attack detection, using the fuzzy inference approach. An effective set of fuzzy rules for inference approach were identified automatically by making use of the fuzzy rule learning strategy, which are more effective for detecting intrusion in a computer network. At first, the definite rules were generated by mining the single length frequent items from attack data as well as normal data. Then, fuzzy rules were identified by fuzzifying the definite rules and these rules were given to fuzzy system, which classify the test data. We have used KDD cup 99 dataset for evaluating the performance of the proposed system and experimentation results showed that the proposed method is effective in detecting various intrusions in computer networks. REFERENCES
[1] Yao, J. T., S.L. Zhao, and L.V. Saxton, "A Study On Fuzzy Intrusion Detection", In Proceedings of the Data Mining, Intrusion Detection, Information Assurance, And Data Networks Security, SPIE, Vol. 5812, pp. 23-30, Orlando, Florida, USA, 2005. [2] Nivedita Naidu and Dr.R.V.Dharaskar, "An Effective Approach to Network Intrusion Detection System using Genetic Algorithm", International Journal of Computer Applications, Vol.1, No.3, pp.26–32, February 2010. [3] J. Allen, A. Christie, and W. Fithen, "State Of the Practice of Intrusion Detection Technologies", Technical Report, CMU/SEI-99-TR-028, 2000. [4] B.V. Dasarathy, "Intrusion Detection", Information Fusion, Vol.4, No.4, pp.243-245, 2003. [5] R.G.Bace, "Intrusion Detection", Macmillan Technical Publishing, Indianapolis, USA, 2000. [6] Marcos M. Campos, Boriana L. Milenova, "Creation and Deployment of Data Mining-Based Intrusion Detection Systems in Oracle Database 10g", in Proceedings of the Fourth International Conference on Machine Learning and Applications, 2005. [7] Anazida Zainal, Mohd Aizaini Maarof and Siti Maryam Shamsudin , "Research Issues in Adaptive Intrusion Detection", in Proceedings of the 2nd Postgraduate Annual Research Seminar (PARS'06), Faculty of Computer Science & Information Systems, Universiti Teknologi Malaysia, 24 – 25 May, 2006. [8] Dr. Fengmin Gong, "Deciphering Detection Techniques: Part II AnomalyBased Intrusion Detection", White Paper from McAfee Network Security Technologies Group, 2003. [9] Susan M. Bridges and Rayford B.Vaughn, "Fuzzy Data Mining And Genetic Algorithms Applied To Intrusion Detection", In Proceedings of the National Information Systems Security Conference (NISSC), Baltimore, MD, pp.16-19, October 2000. [10] Jian Pei, Upadhyaya, S.J., Farooq, F., Govindaraju, V, "Data mining for intrusion detection: techniques, applications and systems ", in Proceedings of the 20th International Conference on Data Engineering, pp: 877 - 87, 2004. [11] Cannady J, "Artificial Neural Networks for Misuse Detection", in Proceedings of the '98 National Information System Security Conference (NISSC'98), pp. 443-456, 1998. [12] Shon T, Seo J, and Moon J, "SVM Approach with A Genetic Algorithm for Network Intrusion Detection", Lecture Notes in Computer Science, Springer Berlin / Heidelberg, Vol. 3733, pp. 224-233, 2005. [13] Yu Y, and Huang Hao, "An Ensemble Approach to Intrusion Detection Based on Improved Multi-Objective Genetic Algorithm", Journal of Software, Vol.18, No.6, pp.1369-1378, June 2007.
Positive class Actual Class Negative class
Predicted class Positive Negative class class True False positive negative (TP) (FN) False positive (FP) True negative (TN)
The evaluation metrics are computed for both training and testing dataset in the testing phase and the obtained result for all attacks and normal data are given in table VII, which is the overall classification performance of the proposed system on KDD cup 99 dataset. By analyzing the result, the overall performance of the proposed system is improved significantly and it achieves more than 90% accuracy for all types of attacks.
Metric Precision Recall F-measure Accuracy Precision Recall F-measure Accuracy Precision Recall F-measure Accuracy Precision Recall F-measure Accuracy Precision Recall F-measure Accuracy
Proposed System Training Testing 0.912522 0.912522 0.37083 0.37083 0.52735457 0.52735457 0.906208 0.909323 0.993563 0.90144 0.94526236 0.9478 0.051948 0.190476 0.08163265 0.992812 0.075949 0.155844 0.10212766 0.991586 0.828439 0.99416 0.90376539 0.910852 0.993828 0.904154 0.94687236 0.949269 0.051948 0.190476 0.08163265 0.993088 0.075949 0.155844 0.10212766 0.991909 0.829318 0.994385 0.90438129 0.903019
http://sites.google.com/site/ijcsis/ ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 8, November 2010
[14] J. Luo, and S. M. Bridges, "Mining fuzzy association rules and fuzzy frequency episodes for intrusion detection", International Journal of Intelligent Systems, Vol. 15, No. 8, pp. 687-704, 2000. [15] W. Lee, S. Stolfo, and K. Mok, "A Data Mining Framework for Building Intrusion Detection Model", In Proceedings of the IEEE Symposium on Security and Privacy, Oakland, CA, pp. 120-132, 1999. [16] Dewan Md. Farid and Mohammad Zahidur Rahman, "Anomaly Network Intrusion Detection Based on Improved Self Adaptive Bayesian Algorithm", Journal of Computers, Vol.5, No.1, January, 2010. [17] K.Yoshida, "Entropy Based Intrusion Detection", in Proceedings of the IEEE Pacific Rim Conference on Communications, Computers and signal Processing, Vol. 2, pp. 840 – 843, Aug 28- 30, 2003. [18] Sujaa Rani Mohan, E.K. Park, Yijie Han, "An Adaptive Intrusion Detection System Using A Data Mining Approach", White paper from University of Missouri, Kansas City, October 2005. [19] Rasha G. Mohammed Helali, "Data Mining Based Network Intrusion Detection System: A Survey", In Novel Algorithms and Techniques in Telecommunications and Networking, pp. 501-505, 2010. [20] Pakkurthi Srinivasu, P.S. Avadhani, Vishal Korimilli, Prudhvi Ravipati, "Approaches and Data Processing Techniques for Intrusion Detection Systems", Vol. 9, No. 12, pp. 181-186, 2009. [21] G. Macia Fernandez and E. Vazquez, "Anomaly-based network intrusion detection: Techniques, systems and challenges", Computers & Security, Vol. 28, No. 1-2, pp. 18-28, February-March 2009. [22] Mark Crosbie and Gene Spa Ord, "Defending a Computer System using Autonomous Agents", Technical report, 1995. [23] Honig, A., Howard, A., Eskin, E., and Stolfo, S. J., "Adaptive Model Generation: An Architecture for the Deployment of Data Mining-Based Intrusion Detection Systems, Applications of Data Mining in Computer Security, Kluwer Academic Publishers, Boston, MA, pp. 154-191, 2002. [24] Stephen F. Owens, Reuven R. Levary, "An adaptive expert system approach for intrusion detection", International Journal of Security and Networks, Vol: 1, No: 3/4, pp: 206-217, 2006. [25] Alok Sharma, Arun K. Pujari, Kuldip K. Paliwal, "Intrusion detection using text processing techniques with a kernel based similarity measure", Computers & Security, Vol: 26, No: 7-8, pp: 488-495, 2007. [26] Shi-Jinn Horng, Ming-Yang Su, Yuan-Hsin Chen, Tzong-Wann Kao, Rong-Jian Chen, Jui-Lin Lai, Citra Dwi Perkasa, "A novel intrusion detection system based on hierarchical clustering and support vector machines", Expert Systems with Applications, Vol: 38, No: 1, pp: 306313, 2011. [27] Abadeh, M.S., Habibi, J., "Computer Intrusion Detection Using an Iterative Fuzzy Rule Learning Approach", in Proceedings of the IEEE International Conference on Fuzzy Systems, pp: 1-6, London, 2007. [28] Bharanidharan Shanmugam, Norbik Bashah Idris, "Improved Intrusion Detection System Using Fuzzy Logic for Detecting Anamoly and Misuse Type of Attacks", in Proceedings of the International Conference of Soft Computing and Pattern Recognition, pp: 212-217, 2009. [29] O. Adetunmbi Adebayo, Zhiwei Shi, Zhongzhi Shi, Olumide S. Adewale, "Network Anomalous Intrusion Detection using Fuzzy-Bayes", IFIP International Federation for Information Processing, Vol: 228, pp: 525530, 2007. [30] Arman Tajbakhsh, Mohammad Rahmati, Abdolreza Mirzaei, "Intrusion detection using fuzzy association rules", Applied Soft Computing, Vol: 9, No: 2, pp: 462-469, 2009. [31] Zhenwei Yu, Tsai, J.J.P., Weigert, T., "An Automatically Tuning Intrusion Detection System", IEEE Transactions on Systems, Man, and Cybernetics, Vol: 37, No: 2, pp: 373 - 384, 2007. [32] Qiang Wang and Vasileios Megalooikonomou, "A clustering algorithm for intrusion detection", in Proceedings of the conference on Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security, vol. 5812, pp. 31-38, March 2005. [33] Cordon O, Gomide F, Herrera F, Hoffmann F, Magdalena L, "Ten years of genetic fuzzy systems: current framework and new trends", Fuzzy Sets and Systems, vol.141, no.1, pp. 5–31, 2004. [34] M. Saniee Abadeh, J. Habib and C. Lucas, "Intrusion detection using a fuzzy genetics-based learning algorithm", Journal of Network and Computer Applications, vol.30, no.1, pp. 414–428, 2007.
[35] R. Agrawal, T. Imielinski, A., Swami, "Mining association rules between sets of items in large databases", in Proceedings of 1993 ACM SIGMOD Intl. Conf. on Management of Data, Washington, DC, pp. 207–216, 1993. [36] http://www.ll.mit.edu/mission/communications/ist/corpora/ideval/data/ 1998data.html [37] http://www.sigkdd.org/kddcup/index.php?section=1999&method=data [38] Mahbod Tavallaee, Ebrahim Bagheri, Wei Lu and Ali A. Ghorbani, "A detailed analysis of the KDD CUP 99 data set", in Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications, pp. 53-58, Ottawa, Ontario, Canada, 2009. [39] Zadeh, L.A., "Fuzzy sets", Information and control, vol.8, pp. 338-353, 1965. [40] Jiawei Han, Jian Pei, Yiwen Yin, Runying Mao, "Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach", Data Mining and Knowledge Discovery, Vol: 8, No: 1, pp: 53 - 87, 2004. R. Shanmugavadivu received her B.Sc. and M.Sc (computer science) degrees from the Department of Computer Science, PSG College of Arts & Science, Coimbatore .Affiliated to Bharathiyar University in 1995 and 1998, respectively. Completed her M.PHIL degree in Computer science in 2008, at Bharathiyar University, Coimbatore .She is currently working as Assistant professor, Department of Computer Science at PSG College of Arts & Science, Coimbatore.
Dr.N.Nagarajan, received his M.E., and B.Tech., degrees in the disciplines of Electronics Engineering from Madras Institute of Technology, under Anna University, Chennai in 1984 and 1982 respectively. He Obtained Ph.D. degree from Anna University, Chennai in "Faculty of Information and Communication Engineering" in 2006..He posses 25 years of teaching experience in various reputed Engineering colleges viz., Kongu Engineering College, Sri Krishna College of Engineering and Technology, Sri Ramakrishna Engineering College etc., .Currently, he is working as a Principal of Coimbatore Institute of Engineering and Information Technology, Coimbatore. He has Published 15 papers in the International refereed journals and 20 papers in International and National conferences. He is a Reviewer for WSEAS International Transactions. He had received a grant of Rupees Five Lakhs in MODROB scheme from AICTE, New Delhi for modernizing Communication Laboratory using Fibre Optic Communication at Kongu Engineering College, Perundurai, Erode, Tamilnadu during the year 1999.He was Selected in, "2000 Outstanding Scientists" for the year 2008-2009 by International Biographical Centre, Great Britain in its 34th edition. He was also nominated as "International Scientist of the year "for 2008 by International Biographical Centre, Cambridge, England.
http://sites.google.com/site/ijcsis/ ISSN 1947-5500


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16428 N 7TH Place, Phoenix, AZ 85022
3 beds, 2.5 baths, 2,291 sqft
Status: Active
Property Description
Want a high end remodel without a high end price? Welcome home! This beautiful 3 bed, 2.5 bath home in Greentrails provides nearly instant access to the 101 Loop & I17. The updates begin at the front gate, added in 2015 creating a lovely courtyard. Inside, the gorgeous kitchen boasts new cabinets, fixtures, appliances and 3cm granite with full back splash. Each bathroom was updated to include modern lighting, cabinets, mirrors, granite counters & tiled showers. Even the garage with evap cooler has a new insulated overhead door and utility sink. Your perfectly manicured yard features lush grass & low maintenance landscaping enclosed by a stuccoed & painted block wall. Add a new roof, water heater, AC & condensation pump all in the last 14 months and this beauty is truly move in ready.
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Eat-in Kitchen
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Microwave, Wall Oven, Refrigerator, Multiple Ovens, Disposal, Dishwasher, Electric Range/Cooktop
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Exterior / Lot Features for 16428 N 7TH Place Phoenix, AZ 85022
2 Garage Spaces, 2 Parking Spaces, Attached Garage Cabinets, Electric Door Opener, Direct Entry from Garage
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From Greenway & 7th St, go south on 7th St, east on Aire Libre & south on 7th Pl. Your new home is on the corner of Aire Libre & 7th Pl.
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Hotels in Ureshino
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There are not so many hotel rooms in Ureshino, just 22, still everyone can find an offer to suit his choice. The most common offers in this city are 3 и 4 stars, they count 18 out of 22, and their prevalent location is central district. According to our practice, it is the most profitable to book a hotel in the east of Ureshino, as it is the location of the hotels with optimal cost and quality offers along with the cheap hotels. Taking into account the small size of the city, there is no need to dwell hotel reservation on the location: the road to the city center would take less then half an hour, so one can book a hotel in any district of Ureshino. The most populated with hotels district of Ureshino is the south, 65% of cheap hotel rooms are concentrated here. Ureshino is an ideal place for family and couple, such travellers rate hotels there 8.0 out of 10 on average. In Ureshino one can enjoy the beauty of Yutoku Inari Shrine. Moreover, there is a choice of 4 hotels, located within a walking distance from these attractions, however they might not be the cheap ones. The view to river from the hotel room can serve as a pleasant addition to the journey. Apart from all these, hotels in Ureshino offer their customers restaurant/cafe, parking, wi-fi in public areas and bar. The staff of the most hotels is multilingual: it speaks english. The most appreciated amenities in Ureshino hotels are cleanliness and value for money. According to the visitors reviews, hotels in Ureshino has delicious food, great wellness & sports facilities and awesome vibe. Take a look at our selection of the cheap hotel rooms in Ureshino to make a well-informed choice for your reservation!
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Hotfrog INDIA provides information regarding Ashish Garg in Gurgaon Haryana. Ashish Garg is located at National Highway 8 NH8 , 1099, Sector 46 Opposite Unitech Cyber Park Huda City Centre Metro Station Main Road, Gurgaon Haryana 122003 and provides school services. Contact them on +91 9818 777 289 or by visiting them on their website http://www.futuristashish.com.
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Robert Bobby Thomas
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"Thinking of you today and on days not near or on your birthday Father. People say I have so many ways like you and Uncle Frank. I trust that in death there is at least no more suffering from life and other complexities? Anyway, today would have been your 82 birthday. God bless you and keep you always. Your son: Gregory J Malone.
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The Oklahoma School for the Deaf Satellite Preschools service pre-k through 1st grade students who are unable to travel to our main campus.
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The Oklahoma School for the Deaf Elementary School services grades pre-k through 6th grade.
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October 04, 2009
Opponent respect for Peyton Manning(notes) has grown to the extent that teams facing the future Hall-of-Famer now feel uncomfortable speaking in his presence! Well, at least the Seahawks will keep quiet when they face the Colts at Lucas Oil Stadium on Sunday. Seattle head coach Jim Mora and defensive coordinator Gus Bradley have implemented a series of silent adjustments for their players, because of Manning's ability to discern changes to enemy defenses based on vocal calls. Instead of calling the defensive plays into the helmet of defensive captain Lofa Tatupu(notes), who would ordinarily call them out to everyone, Seattle's defensive staff will give the play to Tatupu, who will signal his teammates. According to Eric Williams of the Tacoma News Tribune, the Seahawks' defenders will all wear cheat sheets on their wrists, giving them the signals that way.
"Peyton is a guy who will hear (signals) and he'll pick up on it," Mora told the media this week. "We don't want to give him a pre-snap read, and we certainly don't want to tell him, 'Hey, Peyton, we're going to play this coverage.' We're going to try to be silent and nonverbal in our communication."
Mora said that the Seahawks will have a full arsenal of calls, but I wonder if the attempt to fool Manning will backfire on the Seahawks. They'll have enough to deal with just keeping the Colts' offense under wraps, much less deciphering their own signal flares and Morse codes and whatnot. Especially since no quarterback is better at keeping the pre-snap adjustments going to the verrrrrrry last millisecond -- I have a feeling the Seahawks will be spending too much time zigging as Peyton zags, leading to a Seahawks slaughter.
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Little Less Cody Means Improvement
"I feel I did all right, but I feel I can still get better," Alabama's Terrence Cody said following the A-Day scrimmage. "It was disappointing coming out on passing downs. I hate coming out when I'm already hyped up, getting in a groove, and making plays. It really disappoints me when I have to come out."
Cody caught the eyes of the crowd, his teammates and Tide fans with an open field tackle on running back Terry Grant in the A-Day Game. Cody ran down the quick Grant on the sweep.
"I'm trying to convince Coach Saban into putting me at Jack linebacker or defensive end," grinned the always jovial Cody. "I like making plays in open space. That makes me look good, if I make the play. That just helps me out."
One point Alabama Coach Nick Saban made following A-Day was that some people on the Tide defense aren't as good as they seem to think they are. Cody is not likely one Saban had in mind, but Cody got the point, nonetheless.
"We've got to come out with high tempo and energy and stuff," said Cody. "Coach Saban gets very disappointed when the ones that are supposed to be leaders, the ones that are good on defense, and the ones that are supposed to step up and make plays and have good tempo, don't."
It's likely with a new quarterback, running back, and retooled line on offense that the Tide defense will have to keep the team in games, especially early in the 2009 season. Cody feels that the stop troops will be up to that task.
"We've still got a long way to go," Cody said. "We've played good up front, but we can get even better, and have more success than we did (in the spring). From day one of spring practice, I knew we were going to have a good defense, because we have people that want to play, and want to make plays, and get out there and play hard."
As good a spring as the defense had, dominating the offense most scrimmages, Cody knows it can be better
"We're a good defense, when we execute right, and do what we're supposed to do," said the 6-foot-5, 255-pound rising senior, "but sometimes we have flaws and don't execute right. That's what practice is for. We'll just work on that, and get better."
At some positions on the defense, notably the secondary, there are some players who will have to play more than they did in 2008, and as the younger plays learn they make mistakes. Cody tries to mentor such guys.
"We've just got to keep a positive attitude," said Cody. "I just tell them, 'Good job. Don't worry about that play. Worry about the next play.'"
Cody has had a little experience in Alabama's "Wild Elephant" formation on offense. He's a fullback-type blocker in special short yardage situations. But it's not from that perspective Cody had judged junior quarterback Greg McElroy. Cody has seen enough to know that there doesn't have to be an announcement by Nick Saban for the team to know who its quarterback is.
It's McElroy.
"He's done a lot of good," Cody noted, "after living in the shadow of J.P. (John Parker Wilson) the past few years. He's a good player. You put the ball in his hands, and he's going to control the clock and make plays and execute.
"He deserved that (co-MVP) award he got (at A-Day), because he makes some big plays."
Cody got an award, too. He was a player selected by the coaching staff for the Lee Roy Jordan Headhunter Award for his work during spring practice.
The man they call "Mount Cody" is projected as a first round pick in the 2010 NFL draft, and many were surprised he didn't make the leap following his first season in Tuscaloosa. Last year, Cody started all 12 games he was healthy for, and made 24 tackles. He was credited with 4.5 tackles for loss, half a sack, a forced fumble and one quarterback hurry.
Cody was a consensus first-team All-America and All-SEC performer.
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text:
Revision date: October 21st, 2015
General Terms and Conditions
Acision Secure Communications Gmbh, a Xura, Inc. Company (hereinafter: "Xura"), acting as a technical service provider, provides services for its customers (hereinafter: "customers") on the basis of a service agreement. These "General Contractual Terms and Conditions of Acision Secure Communications GmbH, a Xura Company", will apply if the contractual parties make these an integral contractual component by way of appending them to the service agreement. In this case the "Standard Terms and Conditions" of Acision Secure Communications will not apply. The Customer's alternative terms and conditions will not apply. Such terms and conditions will remain inapplicable even if Acision, a Xura company, does not explicitly repudiate them.
1 Establishment of specific business arrangements and contractual purpose
1.1 Business arrangements between the Customer and Xura concerning specific services will be established through the conclusion of a corresponding service agreement and supplementary individual orders to existing service agreements made between the Customer and Xura. Xura is not obliged to accept such requests from the Customer. Sentence 2 correspondingly applies for requests from the Customer for additions or amendments to services.
1.2 When concluding agreements with Xura, the Customer acts exclusively in exercising its commercial or freelance employment activities. It is not permitted to use Xura services for other purposes.
2 Nature and type of service provision
2.1 The Xura Messaging Platform provides its customers with interface and application-based integration solutions enabling messages and short messages (SMS) to be sent and received worldwide as well as complementary services and solutions for supporting business processes.
2.2 Xura is entitled to commission third parties with the fulfillment of its contractual obligations or to otherwise appoint third parties for this purpose.
2.3 Due to the technical circumstances, it is not possible to guarantee uninterrupted availability of the services to be delivered by Xura. Within the sphere of its responsibility, Xura will however ensure that the availability times agreed with the Customer are actually maintained. Details concerning maintenance, system availability and elimination of faults are regulated in the service agreement.
2.4 If the content of a service is typically performed in such a way that Xura commissions third parties with the actual subsequent performance, Xura performs this service by passing on a corresponding order in its own name to the third party (sub-contract). This applies particularly to the transmission of messages. These services will be performed by telecommunications service providers (particularly mobile network providers, internet providers, land line providers) or other service providers (particularly within the definition of Section 3(6) Telecommunications Act (TKG)). In these cases the liability of Xura is limited to the careful selection and instruction to sub-contractors and to the assignment of Xura's applicable claims against third parties.
3 Duties of the Customer
3.1 If the Customer wishes to avail of a Xura service, it must present Xura with a written description of its service, particularly the contents to be transmitted within the terms of the service, the planned sequence and the selected countries. Xura may demand additional information from the Customer. Xura is entitled to provide to other parties (sub-contractors) the information specified in sentences 1 and 2 (e.g. telecommunications service providers and other service providers) to the extent this is required for the delivery of the service in question.
3.2 The Customer is only able to avail of a service and publish its service, once Xura has notified the Customer of the activation. It cannot be guaranteed that a service will be activated as applied for. The activation of a service on the basis of the presented written description does not affect the Customer's liability for its legal responsibility for the service. The Customer must, in particular, comply with the codes of conduct and communications guidelines applicable in the relevant countries.
3.3 Following the commissioning of services, the Customer is duty bound to notify Xura in writing of any amendments to the description presented at the outset.
3.4 Upon request, the Customer will at any time promptly disclose the necessary information concerning the type and method of the use of the services commissioned. The Customer is aware that any delay to the disclosure of information may result in Xura sustaining significant losses.
3.5 Unless a separate agreement has been made, the Customer may only avail of Xura services for its own purposes, with the exception of affiliated companies within the definition of Section 15 German Stock Corporation Act. It is therefore prohibited, in particular, from using Xura services under commission or for the account of any third party, unless another agreement has been expressly made.
3.6 The Customer may use the call numbers and short code numbers provided to it solely for those activated services for which they are contractually provided. The entire communication of the Customer with end customers within the terms of a service must be implemented by way of an interface provided to it by Xura for this purpose and upon disclosure of the designated sender identifier.
3.7 The Customer must ensure that the sales tax chargeable on the services it delivers to its end customers is collected and remitted in accordance with the applicable law in the relevant country.
3.8 All obligations are likewise applicable for affiliated companies as defined by Section 15 German Stock Corporation Act (AktG. The Customer must ensure that all obligations under this service agreement including all its annexes are also observed by the affiliated companies or third parties. Paragraph 3.5 remains unaffected.
4 Compliance with statutory regulations
4.1 The Customer alone bears the legal responsibility, particularly the press law and competition law responsibility for its services (e.g. publication of images, message contents), irrespective of the route by which the content is provided. The Customer is responsible for duly establishing contractual relationships with the end customers and the proper performance of all the obligations resulting therein.
4.2 The Customer is not entitled to avail of Xura services for content, services or activities,
It is prohibited in particular to avail of services for content, services or activities,
4.3 In the event that it becomes aware of any content connected with its service that is in violation of Paragraph 4.2, the Customer must ensure that such content is removed promptly.
4.4 In relation to the offer of services which may not be made available to end customers within a particular age group, the Customer must offer the services together with an effective access control so that use is only available to end customers who are of the permitted age.
4.5 The Customer is obliged to refrain from sending end customers unsolicited or unwanted message (e.g. unwanted advertising mails, computer viruses or chain mails). Damage or losses, particularly contractual penalties, fees or charges incurred by Xura or the Customer by way of a violation of the duty defined in sentence 1 will be borne solely by the Customer, unless the unwanted delivery of messages is exclusively attributable to the culpability of Xura. In the event that a claim is raised against Xura due to damage within the definition of sentence 2, the Customer must compensate all damage sustained including the reasonable costs of a legal defense (5.1).
4.6 The Customer guarantees that it possesses all rights of use (particularly copyright, trademark rights, ancillary copyright, personality and other rights) to the content communicated by it (e. g. texts, photos, graphics, audio storage media and video tapes etc.) required to activate the relevant service. The rights of use must include a corresponding dispatch or conversion into the required format (e.g. SMS).
4.7 In the delivery of its services, the Customer must satisfy all the duties to provide information according to the applicable law of the relevant country (e.g. in Germany, the duties to provide information pursuant to Section 5 Telemedia Act (TMG), Section 312c German Civil Code (BGB) in conjunction with the German Civil Code Regulation on the Obligation to Provide Information (BGBInfoV), Price Information Regulation. The Customer, moreover, is required to inform the end customers that it, and not Xura, is responsible for the relevant service.
4.8 Xura is entitled, but not obliged, to examine the contents for which the Customer is responsible. Xura is entitled to refuse the activation of services that violate Paragraph 4. The activation of services by Xura does not affect the duties of the Customer according to this Paragraph 4. Xura will not provide the Customer with any legal services within the definition of Section 2 Legal Services Act. In the event that in specific cases Xura provides the Customer with guidance concerning the arrangement of its services, this then solely relates to the relay of Xura's knowledge of the market. The Customer is independently responsible for verifying, or arranging the verification, the legal admissibility of its services.
4.9 In the event that the service of the Customer is delivered in combination with an internet site, the Customer is responsible for ensuring that its own internet site is effectively protected against external interference or misuse. In the event that hacking or other such attacks or cases of misuse result in Xura sustaining any damage, the Customer will be fully liable to compensate this.
4.10 In order to prevent misuse through internal or external individuals, the Customer's network and the programmed application must be secured against third-party attacks or internal attacks in accordance with the standards customary within the industry. The Customer's application and the input interface (storage, activation of call numbers) fulfill the customary industry and quality standards and will be maintained at this level. In the event that messages or SMS are sent from an insecure internet area, the Customer must deploy suitable security measures to protect against misuse (such as Captcha for example). The Customer must implement suitable measures for the early identification and preclusion of errors in relation to contacting interfaces (e.g. unwanted multiple message or SMS dispatch, wrong call numbers, etc.). The Customer must treat as confidential the log-in data provided to it by Xura and it must securely store it safely from access by other individuals.
4.11 If the Customer significantly or persistently violates the obligations set out in this Paragraph 4, and if the Customer fails to remedy this contractually prohibited conduct within a reasonable period of time despite having received a warning, Xura will then be entitled to block the services under this agreement. In such a case, the Customer remains obliged to pay the fees for the transmitted messages irrespective of any claims to damage or reimbursement of costs.
5 Reimbursement of costs / invoices / limitation on set-off
5.1 All expenses that Xura incurs in connection with the provision of the services for the Customer must be reimbursed by the Customer, insofar as Xura was entitled to consider these expenses as necessary given the circumstances (Section 675(1), Section 670 German Civil Code (BGB)). Instead of reimbursement, in accordance with Section 257 German Civil Code (BGB) may demand to be indemnified in respect of any liability entered into in this context. Reimbursable costs for the purpose of Sentence 1 particularly include contractual penalties and other charges passed onto Xura by a network operator or other service providers on the basis of the contracts to be concluded under Paragraph 1.1, insofar as these contractual penalties or other charges are incurred by way of services of the Customer, as well as other reasonable costs of the legal defence incurred in this context. Reimbursable costs particularly include the costs of the legal defence against end customer claims in the event of violations on the part of the Customer against any obligation contained in Paragraph 4. The Customer is aware that in individual instances the contractual penalties and charges could exceed many times over the income that the Customer generates from its services|.
5.2 The Customer is required to promptly examine all invoices issued to it by Xura and to promptly inform Xura of any objections it has, but in any case no later than within a period of six (6) weeks following receipt of the relevant invoice. Following the expiry of this time period, the invoice is deemed accepted. Xura will explicitly refer to this consequence in the invoice. The Customer may demand that the invoice be corrected even after this time period has expired. In that case it must however prove that the invoice was inaccurate or incomplete.
5.3 All invoices are due for immediate payment by the Customer following receipt. The Customer will be automatically (without requirement of any additional notice) in default of payment fourteen (14) days after having received an invoice. Xura is entitled to set-off its receivables due from the Customer against any counterclaims that the Customer may have against it.
5.4 The Customer is entitled to exercise a set-off against Xura only if its counterclaim is not disputed or has been confirmed by a final court judgement.
6 Liability of Xura
6.1 Within the course of delivering the services, Xura is liable to the Customer only for deliberate acts or gross negligence on the part of Xura or for death or personal injury or for the slightly negligent breach of a significant contractual obligation that jeopardises the contractual purpose. A significant contractual obligation is an obligation the fulfilment of which is crucial for the successful implementation of the services to be delivered by Xura.
6.2 The liability of Xura vis-à-vis the Customer for the slightly negligent breach of a significant contractual obligation is limited to typical damage for this type of agreement foreseeable at the time of contractual conclusion.
6.3 Xura is not liable for lost profits, indirect damage or other forms of consequential damage.
6.4 In the event that Xura, as a provider of telecommunications services, has an obligation to compensate a financial loss and this is not based on a deliberate act, the liability will be determined according to Section 44a Telecommunications Act (TKG) in deviation to 6.1 and 6.2.
7 Non-disclosure, data protection
7.1 Each of the parties is required to treat as strictly confidential all information or documentation each receives from the other party (hereinafter: "information provider") prior to and following the establishment of the business arrangement, or otherwise received from that party and which relate to the business relationship or its terms and conditions, along with all other information and documentation (hereinafter: "confidential information"). This applies irrespective of whether or not this information is labelled "confidential" or which may otherwise be reasonably regarded as being confidential.
7.2 Neither party will apply or disclose confidential information vis-à-vis their respective employees or contractual partners or pass it on to other parties (especially affiliated companies of the parties), unless this is necessary for the fulfilment of their respective contractual duties to the other party. The preceding sentence does not apply for the disclosure of confidential information to consultants bound by a legal professional duty of confidentiality. Prior to any disclosure of confidential information, the parties will inform every recipient of its confidential nature and they will enjoin every recipient to non-disclosure obligations as rigorous as those provided for in this Paragraph 7.
7.3 The non-disclosure obligation does not apply in relation to confidential information already known to the receiving party independently from the conclusion or execution of a contract with the other party, is already in the public domain without any breach by the receiving party of any obligation contained in this Paragraph 7 or which must be disclosed under the law or by way of an order issued by a court or public authority. In such a case the party required to disclose the said information will provide reasonable assistance to the other party.
7.4 All confidential information remains the property of the information provider and may only be copied or reproduced with its prior written consent. Upon demand by the information provider the other party must return or destroy the confidential information along with all media containing it. In the latter case the destruction of the information must be confirmed in writing.
7.5 Both parties undertake to comply with all applicable laws (as may be amended from time to time) pertaining to the protection of personal data during the collection, processing and use thereof.
7.6 The aforementioned obligations remain applicable after the end of the business arrangement, until such time that the receiving party proves that the confidential information has entered the public domain in a manner not constituting a breach by the receiving party of the duties contained in this Paragraph 7.
8 Contractual term, rights of cancellation
8.1 The business arrangement between the Customer and Xura will run indefinitely unless it is ended in accordance with the cancellation rights provided for in the service agreement.
8.2 Xura has the right to wholly or partially suspend the delivery of services to the Customer, if the Customer significantly breaches its duties under the business arrangement with Xura (a breach by the Customer of the duties contained in Paragraph 4 will always be deemed significant) or if a telecommunications service provider, other service provider or relevant public authority demands this of Xura. In the event that services are suspended, Xura will inform the Customer promptly. The Customer must inform Xura once it has rectified its breach of duty. Xura will inform the Customer if it deems the breach to be rectified, and if so it will recommence delivery of service.
8.3 Xura may, with a notice period of seven (7) days, cancel the services hereby affected if it ceases providing these services or if a telecommunications service provider or other service provider seeks to end a contract with Xura or has already ended it, and this contract is necessary for the delivery of the services.
8.4 The right of both parties to extraordinary termination of this agreement (cancellation for cause) remains unaffected. Xura is particularly entitled to exercise an extraordinary termination of the whole or part of the business arrangement with the Customer if
8.5 Any cancellation must be issued in writing, whereby communication by email is excluded.
8.6 Following the end of its business arrangement with Xura, the Customer has no entitlement to retain the call numbers, short code numbers or data provided to it. In the event that following the end of the business arrangement with Xura, a migration of the supplied call and/or short code numbers to another service provider does occur, a separate agreement must be concluded to provide for this arrangement and any requisite migration of data stocks and the time and effort will be compensated in accordance with the relevant agreed hourly rate. In the event of a migration of the call and/or short code numbers, the Customer must, in particular, ensure that the corresponding numbers are registered in its name from that time onwards and that no further mention of Xura is made in billing information and other listings.
9 Limitation period
9.1 All claims of the Customer under this joint business arrangement are subject to a limitation period of twelve (12) months. The limitation period commences as soon as the Customer gains knowledge of the circumstances underlying the claim and the identity of the liable party, or from that time it should have done so had it not been grossly negligent. Sentence 1 does not apply for claims for damages relating to death or personal injury or for deliberate or grossly negligent breaches. For the purpose of clarification, sentence 1 does not affect the assertion of non-time-barred claims to damages, even if the underlying service was delivered outside of the limitation period. The critical aspect therefore is the time that the claim for damages is established and not the time that the service is delivered.
10 Contractual amendments
10.1 Xura is entitled to amend contractual agreements made with the Customer (including these Terms and Conditions for Messaging Services, performance specifications and other contractual documentation). Xura will inform the Customer of such changes in writing or by email. Such amendments are deemed to have been accepted by the Customer if it raises no objection in this regard within a time period of six weeks from the receipt of the notification concerning the contractual amendments.
Contrary to Paragraph 10.1, Xura has the right to adjust, with immediate effect, the prices for sending and receiving SMS in general or for specific countries or networks and to inform the Customer of these changes by email. This applies particularly if there are adjustments to the prices of upstream suppliers, such as telecommunications service providers or other service providers. Having received the change notification, the Customer has the right, within five (5) working days, to terminate the services in the destination countries affected. In this case the agreed minimum turnover will be extinguished or reduced by the respectively affected country or network if the Customer ceases sending and receiving in this network within five (5) working days due to the adjustment in price. Xura will explicitly refer to this consequence in the notification.
10.2 In the event that the Customer objects to the implementation of a change as defined in Paragraph 10.1 or 10.2, both parties are then entitled to cancel the business arrangement between Xura and the Customer with a notice period of six weeks, the said cancellation affecting the entire business arrangement or only the services affected by the changes. This does not however exempt the Customer from its independent responsibility to inform itself about changes to the laws and codes of conduct applicable in the relevant countries and to implement updates to the required extent within its service.
11 Notifications, contact persons
11.1 The Customer will nominate experience, expert employees responsible for taking or initiating all requisite decisions, and who are available to Xura as points of contact for consultation.
11.2 The Customer must send all notifications or declarations addressed to Xura to the points of contact agreed in the Annex to the service agreement in order that these notices etc. become effective.
12 Other agreements
12.1 The business arrangement between the Customer and Xura is governed by German law to the exclusion of the principles on conflicts of law and the United Nations Convention on Contracts for the International Sale of Goods. All disputes arising from or connected with this business arrangement will be heard before the competent court in Munich.
12.2 In the event that the Customer has agreed provisions with Xura contrary to these Terms and Conditions, the former will take precedence.
12.3 If any agreed provisions are invalid or ineffective, or become invalid in the future, this will not affect the validity of the remaining provisions. The same applies in the event that it transpires that the agreed provisions contain gaps. Any ineffective or invalid provision will be deemed substituted by a suitable provision which most closely and legally fulfils what the parties would have agreed had they known of the invalidity or ineffectiveness or the original provision; the same applies in relation to any gaps identified in the provisions. If the ineffectiveness of a provision concerns a performance quantity or a measure of time (time period or date), this will be resolved by substituting the ineffective provision at this point with the legally admissible measure or quantity that most closely fulfils the intentions of the parties.
12.4 All headings used in this service agreement and its annexes are only for the purpose of providing coherence and do not have any legal significance or effect.
If you have any questions or concerns regarding privacy at Xura or Acision Secure Communications, a Xura company, please send us a detailed message to firstname.lastname@example.com.
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Ranjit's Kitchen
- Telephone 0141 423 8222
- Website www.ranjitskitchen.com
The 2016/17 edition of The List's Eating & Drinking Guide is out now – only £5.95 (+p&p).
This review is taken from the current (2016) edition.
Another Indian restaurant opens with another claim of providing authentic home cooking. But this time it really is different. Ranjit Kaur, a Punjabi woman who has been cooking samosas and sweets for Asian weddings for years, has opened a modest deli and café in order to share a few of her dishes. The menu consists of a daal and a sabji (veg) dish of the day plus a couple of chickpea and homemade paneer dishes, various fried snacks and a selection of Punjabi teas and coffees. This is not the place to come for a riotous evening of beer and butter chicken, but rather to discover wholesome comfort food, delicate spices and piquant pickles. The light and subtle paneer pakora consists of two thin strips of cheese sandwiching ginger and onion slices, coated in gram flour batter, while the three-dish Punjabi thali is a delight – don't miss the pickled ginger on the side. And chai with a couple of gulab jamun make the perfect finish. The cheery staff make all comers welcome … an added bonus to the terrific food.
- High point: Lime, chilli and salt pickled ginger
- Low point: Wooden tables and chairs … may need a few cushions in winter
- Notable dish: Punjabi thali
- Provides: Vegetarian options (at least ¼ main courses), Children's portions
- Music on stereo: Reggae and chilled Asian sounds
- Capacity: 25
- Largest group: 25
- Open since: 2015
- Number of wines sold by the glass: No alcohol licence


text:
Berkshire Hathaway HomeServices Spencer-Hughes Real Estate
(603) 569-6060
Listing Provided Courtesy Of: Melanson Real Estate - Tim Melanson
Last Updated: 2/24/2017 8:20 PM
Description for Tm 249-1 Pleasant Valley Wolfeboro, NH 03894
A quiet, country setting with a great lot for your new home. Private 2.54 acres on a paved dead end road with owner financing possible.
Listing Information
Electric service at street
Room Information
Exterior / Lot Features
Public Roads, Wooded
Financial Considerations
Inside AccessTM
Current market conditions for Tm 249-1 Pleasant Valley
Active Market Trends
Source:MLS DATA
Median list price$499,000
Days on market207
Properties for sale62
Sold Market Trends
Source:MLS DATA
Median sold price$271,250
Sales to list price9,359.0%
Properties sold44
Median sq ft price$143
Arrows indicate 90 day trend
Listing Price History for Tm 249-1 Pleasant Valley
Wolfeboro Real Estate
Wolfeboro is located in New Hampshire. Wolfeboro, New Hampshire 03894 has a population of 2,425.
The median household income in Wolfeboro, New Hampshire 03894 is $55,294. The median household income for the surrounding county is $50,865 compared to the national median of $53,046. The median age of people living in Wolfeboro 03894 is 60.3 years.
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text:
Periods problem/pregnancy
Hi Sir/Mam, My fiance suffering from non period month and we worry that she might be pregnant. For that we not having intercourse from 1month 12days. I have tried I pills and protection too. But how come it is possible. Next year we are planning to marry. We are not ready for child. So please help!
17 Views
Doctor Answers (1) on Periods problem/pregnancy
Harisha N L
3 yrs. of exp | Bangalore | General Physician
INR 250 for 24 hrsCHAT NOW
Your menstrual cycle is under hormonal control. The ovary usually releases one egg (ovulation) each month, and about two weeks after, the uterine lining sheds, resulting in menstruation. If the egg is fertilized, it is implanted into the uterus and menstruation does not occur. However, if ovulation does not occur regularly, the body does not send signals to the uterus to regulate menstruation. Stress, jet lag, lack of sleep and too much exercise can disrupt your hormonal balance and cause a change in your usual menstrual cycle...Consult nearest gynecologists they will do the needful.
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