Biblio

Situation awareness consists of "the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future". Being aware of the security situation is then mandatory to launch proper security reactions in response to cybersecurity attacks. Security Incident and Event Management solutions are deployed within Security Operation Centers. Some vendors propose machine learning based approaches to detect intrusions by analysing networks behaviours. But cyberattacks like Wannacry and NotPetya, which shut down hundreds of thousands of computers, demonstrated that networks monitoring and surveillance solutions remain insufficient. Detecting these complex attacks (a.k.a. Advanced Persistent Threats) requires security administrators to retain a large number of logs just in case problems are detected and involve the investigation of past security events. This approach generates massive data that have to be analysed at the right time in order to detect any accidental or caused incident. In the same time, security administrators are not yet seasoned to such a task and lack the desired skills in data science. As a consequence, a large amount of data is available and still remains unexplored which leaves number of indicators of compromise under the radar. Building on the concept of situation awareness, we developed a situation-driven framework, called dynSMAUG, for dynamic security management. This approach simplifies the security management of dynamic systems and allows the specification of security policies at a high-level of abstraction (close to security requirements). This invited paper aims at exposing real security situations elicitation, coming from networks security experts, and showing the results of exploratory analysis techniques using complex event processing techniques to identify and extract security situations from a large volume of logs. The results contributed to the extension of the dynSMAUG solution.

Cyber Physical Systems (CPS) operating in modern critical infrastructures (CIs) are increasingly being targeted by highly sophisticated cyber attacks. Threat actors have quickly learned of the value and potential impact of targeting CPS, and numerous tailored multi-stage cyber-physical attack campaigns, such as Advanced Persistent Threats (APTs), have been perpetrated in the last years. They aim at stealthily compromising systems' operations and cause severe impact on daily business operations such as shutdowns, equipment damage, reputation damage, financial loss, intellectual property theft, and health and safety risks. Protecting CIs against such threats has become as crucial as complicated. Novel distributed detection and reaction methodologies are necessary to effectively uncover these attacks, and timely mitigate their effects. Correlating large amounts of data, collected from a multitude of relevant sources, is fundamental for Security Operation Centers (SOCs) to establish cyber situational awareness, and allow to promptly adopt suitable countermeasures in case of attacks. In our previous work we introduced three methods for security information correlation. In this paper we define metrics and benchmarks to evaluate these correlation methods, we assess their accuracy, and we compare their performance. We finally demonstrate how the presented techniques, implemented within our cyber threat intelligence analysis engine called CAESAIR, can be applied to support incident handling tasks performed by SOCs.