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See detailRisk Monitoring and Intrusion Detection for Industrial Control Systems
Muller, Steve UL

Doctoral thesis (2018)

Cyber-attacks on critical infrastructure such as electricity, gas, and water distribution, or power plants, are more and more considered to be a relevant and realistic threat to the European society ... [more ▼]

Cyber-attacks on critical infrastructure such as electricity, gas, and water distribution, or power plants, are more and more considered to be a relevant and realistic threat to the European society. Whereas mature solutions like anti-malware applications, intrusion detection systems (IDS) and even intrusion prevention or self-healing systems have been designed for classic computer systems, these techniques have only been partially adapted to the world of Industrial Control Systems (ICS). As a consequence, organisations and nations fall back upon risk management to understand the risks that they are facing. Today's trend is to combine risk management with real-time monitoring to enable prompt reactions in case of attacks. This thesis aims at providing techniques that assist security managers in migrating from a static risk analysis to a real-time and dynamic risk monitoring platform. Risk monitoring encompasses three steps, each being addressed in detail in this thesis: the collection of risk-related information, the reporting of security events, and finally the inclusion of this real-time information into a risk analysis. The first step consists in designing agents that detect incidents in the system. In this thesis, an intrusion detection system is developed to this end, which focuses on an advanced persistent threat (APT) that particularly targets critical infrastructures. The second step copes with the translation of the obtained technical information in more abstract notions of risk, which can then be used in the context of a risk analysis. In the final step, the information collected from the various sources is correlated so as to obtain the risk faced by the entire system. Since industrial environments are characterised by many interdependencies, a dependency model is elaborated which takes dependencies into account when the risk is estimated. [less ▲]

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See detailA training-resistant anomaly detection system
Muller, Steve UL; Lancrenon, Jean; Harpes, Carlo et al

in Computers & Security (2018), 76

Modern network intrusion detection systems rely on machine learning techniques to detect traffic anomalies and thus intruders. However, the ability to learn the network behaviour in real-time comes at a ... [more ▼]

Modern network intrusion detection systems rely on machine learning techniques to detect traffic anomalies and thus intruders. However, the ability to learn the network behaviour in real-time comes at a cost: malicious software can interfere with the learning process, and teach the intrusion detection system to accept dangerous traffic. This paper presents an intrusion detection system (IDS) that is able to detect common network attacks including but not limited to, denial-of-service, bot nets, intrusions, and network scans. With the help of the proposed example IDS, we show to what extent the training attack (and more sophisticated variants of it) has an impact on machine learning based detection schemes, and how it can be detected. © 2018 Elsevier Ltd [less ▲]

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See detailEfficiently computing the likelihoods of cyclically interdependent risk scenarios
Muller, Steve UL; Harpes, Carlo; Le Traon, Yves UL et al

in Computers & Security (2017), 64

Quantitative risk assessment provides a holistic view of risk in an organisation, which is, however, often biased by the fact that risk shared by several assets is encoded multiple times in a risk ... [more ▼]

Quantitative risk assessment provides a holistic view of risk in an organisation, which is, however, often biased by the fact that risk shared by several assets is encoded multiple times in a risk analysis. An apparent solution to this issue is to take all dependencies between assets into consideration when building a risk model. However, existing approaches rarely support cyclic dependencies, although assets that mutually rely on each other are encountered in many organisations, notably in critical infrastructures. To the best of our knowledge, no author has provided a provably efficient algorithm (in terms of the execution time) for computing the risk in such an organisation, notwithstanding that some heuristics exist. This paper introduces the dependency-aware root cause (DARC) model, which is able to compute the risk resulting from a collection of root causes using a poly-time randomised algorithm, and concludes with a discussion on real-time risk monitoring, which DARC supports by design. © 2016 Elsevier Ltd [less ▲]

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See detailFast and optimal countermeasure selection for attack defence trees
Muller, Steve UL; Harpes, Carlo; Muller, Cédric

in Lecture Notes in Computer Science (2017), 10224 LNCS

Risk treatment is an important part of risk management, and deals with the question which security controls shall be implemented in order to mitigate risk. Indeed, most notably when the mitigated risk is ... [more ▼]

Risk treatment is an important part of risk management, and deals with the question which security controls shall be implemented in order to mitigate risk. Indeed, most notably when the mitigated risk is low, the costs engendered by the implementation of a security control may exceed its benefits. The question becomes particularly interesting if there are several countermeasures to choose from. A promising candidate for modeling the effect of defensive mechanisms on a risk scenario are attack–defence trees. Such trees allow one to compute the risk of a scenario before and after the implementation of a security control, and thus to weigh its benefits against its costs. A naive approach for finding an optimal set of security controls is to try out all possible combinations. However, such a procedure quickly reaches its limits already for a small number of defences. This paper presents a novel branch-and-bound algorithm, which skips a large part of the combinations that cannot lead to an optimal solution. The performance is thereby increased by several orders of magnitude compared to the pure brute–force version. © 2017, Springer International Publishing AG. [less ▲]

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See detailDynamic Risk Analyses and Dependency-Aware Root Cause Model for Critical Infrastructures
Muller, Steve UL; Harpes, Carlo; Le Traon, Yves UL et al

in International Conference on Critical Information Infrastructures Security (2016)

Critical Infrastructures are known for their complexity and the strong interdependencies between the various components. As a result, cascading effects can have devastating consequences, while foreseeing ... [more ▼]

Critical Infrastructures are known for their complexity and the strong interdependencies between the various components. As a result, cascading effects can have devastating consequences, while foreseeing the overall impact of a particular incident is not straight-forward at all and goes beyond performing a simple risk analysis. This work presents a graph-based approach for conducting dynamic risk analyses, which are programmatically generated from a threat model and an inventory of assets. In contrast to traditional risk analyses, they can be kept automatically up-to-date and show the risk currently faced by a system in real-time. The concepts are applied to and validated in the context of the smart grid infrastructure currently being deployed in Luxembourg. [less ▲]

Detailed reference viewed: 73 (6 UL)