Biblio

STAMP (System Theoretic Accident Model and Processes) is one of the theories that has been attracting attention as a new safety analysis method for complex systems. CAST (Causal Analysis using System Theory) is a causal analysis method based on STAMP theory. The authors investigated an information security incident case, “AIST (National Institute of Advanced Industrial Science and Technology) report on unauthorized access to information systems,” and attempted accident analysis using CAST. We investigated whether CAST could be applied to the cyber security analysis. Since CAST is a safety accident analysis technique, this study was the first to apply CAST to cyber security incidents. Its effectiveness was confirmed from the viewpoint of the following three research questions. Q1:Features of CAST as an accident analysis method Q2:Applicability and impact on security accident analysis Q3:Understanding cyber security incidents with a five-layer model.

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.

In times of Industry 4.0 and cyber-physical systems (CPS) providing security is one of the biggest challenges. A cyber attack launched at a CPS poses a huge threat, since a security incident may affect both the cyber and the physical world. Since CPS are very flexible systems, which are capable of adapting to environmental changes, it is important to keep an overview of the resulting costs of providing security. However, research regarding CPS currently focuses more on engineering secure systems and does not satisfactorily provide approaches for evaluating the resulting costs. This paper presents an interaction-based model for evaluating security costs in a CPS. Furthermore, the paper demonstrates in a use case driven study, how this approach could be used to model the resulting costs for guaranteeing security.

This paper presents a system named SPOT to achieve high accuracy and preemptive detection of attacks. We use security logs of real-incidents that occurred over a six-year period at National Center for Supercomputing Applications (NCSA) to evaluate SPOT. Our data consists of attacks that led directly to the target system being compromised, i.e., not detected in advance, either by the security analysts or by intrusion detection systems. Our approach can detect 75 percent of attacks as early as minutes to tens of hours before attack payloads are executed.