IT security community is recently facing a change of trend from closed to open working groups and from restrictive information to full information disclosure and sharing. One major feature for this trend change is the number of incidents and various Indicators of compromise (IoC) that appear on a daily base, which can only be faced and solved in a collaborative way. Sharing information is key to stay on top of the threats. To cover the needs of having a medium for information sharing, different initiatives were taken such as the Open Source Threat Intelligence and Sharing Platform called MISP. At current state, this sharing and collection platform has become far more than a malware information sharing platform. It includes all kind of IoCs, malware and vulnerabilities, but also financial threat or fraud information. Hence, the volume of information is increasing and evolving. In this paper we present implemented distributed data interaction methods for MISP followed by a generic scoring model for decaying information that is shared within MISP communities. As the MISP community members do not have the same objectives, use cases and implementations of the scoring model are discussed. A commonly encountered use case in practice is the detection of indicators of compromise in operational networks.
@cite_0: Across the world, organizations have teams gathering threat data to protect themselves from incoming cyber attacks and maintain a strong cyber security posture. Teams are also sharing information, because along with the data collected internally, organizations need external information to have a comprehensive view of the threat landscape. The information about cyber threats comes from a variety of sources, including sharing communities, open-source and commercial sources, and it spans many different levels and timescales. Immediately actionable information are often low-level indicators of compromise, such as known malware hash values or command-and-control IP addresses, where an actionable response can be executed automatically by a system. Threat intelligence refers to more complex cyber threat information that has been acquired or inferred through the analysis of existing information. Information such as the different malware families used over time with an attack or the network of threat actors involved in an attack, is valuable information and can be vital to understanding and predicting attacks, threat developments, as well as informing law enforcement investigations. This information is also actionable, but on a longer time scale. Moreover, it requires action and decision-making at the human level. There is a need for effective intelligence management platforms to facilitate the generation, refinement, and vetting of data, post sharing. In designing such a system, some of the key challenges that exist include: working with multiple intelligence sources, combining and enriching data for greater intelligence, determining intelligence relevance based on technical constructs, and organizational input, delivery into organizational workflows and into technological products. This paper discusses these challenges encountered and summarizes the community requirements and expectations for an all-encompassing Threat Intelligence Management Platform. The requirements expressed in this paper, when implemented, will serve as building blocks to create systems that can maximize value out of a set of collected intelligence and translate those findings into action for a broad range of stakeholders.
Information sharing is related to a lot of challenges as presented in @cite_0 , where requirements and needs for successful threat intelligence platforms, such as the added value of shared data and privacy, are discussed. Other requirements are for example a new quality control approaches.