attack graphs

PhD-Theses: Improving resilience to cyber-attacks by analysing system output impacts and costs

Jukka Soikkeli

Abstract

Cyber-attacks cost businesses millions of dollars every year, a key component of which is the cost of business disruption from system downtime. As cyber-attacks cannot all be prevented, there is a need to consider the cyber resilience of systems, i.e. the ability to withstand cyber-attacks and recover from them.

Previous works discussing system cyber resilience typically either offer generic high-level guidance on best practices, provide limited attack modelling, or apply to systems with special characteristics. There is a lack of an approach to system cyber resilience evaluation that is generally applicable yet provides a detailed consideration for the system-level impacts of cyber-attacks and defences.

We propose a methodology for evaluating the effectiveness of actions intended to improve resilience to cyber-attacks, considering their impacts on system output performance, and monetary costs. It is intended for analysing attacks that can disrupt the system function, and involves modelling attack progression, system output production, response to attacks, and costs from cyber-attacks and defensive actions.

Studies of three use cases demonstrate the implementation and usefulness of our methodology. First, in our redundancy planning study, we considered the effect of redundancy additions on mitigating the impacts of cyber-attacks on system output performance. We found that redundancy with diversity can be effective in increasing resilience, although the reduction in attack-related costs must be balanced against added maintenance costs. Second, our work on attack countermeasure selection shows that by considering system output impacts across the duration of an attack, one can find more cost-effective attack responses than without such considerations. Third, we propose an approach to mission viability analysis for multi-UAV deployments facing cyber-attacks, which can aid resource planning and determining if the mission can conclude successfully despite an attack. We provide different implementations of our model components, based on use case requirements.

https://spiral.imperial.ac.uk/handle/10044/1/100910

Responding to Attacks and Compromise at the Edge (RACE)

IoT systems evolve dynamically and are increasingly used in critical applications. Understanding how to maintain the operation of the system when systems have been partially compromised is therefore of critical importance. This requires to continuously assess the risk to other parts of the system, determine the impact of the compromise and to select appropriate mitigation strategies to respond to the attack. The ability to cope with dynamic system changes is a key and significant challenge in achieving these objectives.

RACE is articulated into four broad themes of work: understanding attacks and mitigation strategies, maintaining an adequate representation of risk to the other parts of the system by understanding how attacks can evolve and propagate, understanding the impact of the compromise upon the functionality of the system and selecting countermeasure strategies taking into account trade-offs between minimising disruption to the system operation and functionality provided and minimising the risk to the other parts of the system.

Analyzing the Viability of UAV Missions Facing Cyber Attacks

With advanced video and sensing capabilities, un-occupied aerial vehicles (UAVs) are increasingly being usedfor numerous applications that involve the collaboration andautonomous operation of teams of UAVs. Yet such vehiclescan be affected by cyber attacks, impacting the viability oftheir missions. We propose a method to conduct mission via-bility analysis under cyber attacks for missions that employa team of several UAVs that share a communication network.We apply our method to a case study of a survey mission ina wildfire firefighting scenario. Within this context, we showhow our method can help quantify the expected missionperformance impact from an attack and determine if themission can remain viable under various attack situations.Our method can be used both in the planning of themission and for decision making during mission operation.Our approach to modeling attack progression and impactanalysis with Petri nets is also more broadly applicable toother settings involving multiple resources that can be usedinterchangeably towards the same objective.

J. Soikkeli, C. Perner and E. Lupu, “Analyzing the Viability of UAV Missions Facing Cyber Attacks,” in 2021 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), Vienna, Austria, 2021 pp. 103-112.
doi: 10.1109/EuroSPW54576.2021.00018

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CONCORDIA: Cyber Security Competence Network for Research and Innovation (Concordia)

A Cybersecurity Competence Network with leading research, technology, industrial and public competences. CONCORDIA provides excellence and leadership in technology, processes and services to establish an user-centric EU-integrated cyber security ecosystem for digital sovereignty in Europe.

Bayesian Attack Graphs for Security Risk Assessment

Attack graphs offer a powerful framework for security risk assessment. They provide a compact representation of the attack paths that an attacker can follow to compromise network resources from the analysis of the network topology and vulnerabilities. The uncertainty about the attacker’s behaviour makes Bayesian networks suitable to model attack graphs to perform static and dynamic security risk assessment. Thus, whilst static analysis of attack graphs considers the security posture at rest, dynamic analysis accounts for evidence of compromise at run-time, helping system administrators to react against potential threats. In this paper, we introduce a Bayesian attack graph model that allows to estimate the probabilities of an attacker compromising different resources of the network. We show how exact and approximate inference techniques can be efficiently applied on Bayesian attack graph models with thousands of nodes.

Luis Muñoz-González, Emil C. Lupu, “Bayesian Attack Graphs for Security Risk Assessment.” IST-153 NATO Workshop on Cyber Resilience, 2017.

Efficient Attack Graph Analysis through Approximate Inference

Luis Muñoz-González, Daniele Sgandurra, Andrea Paudice, Emil C. Lupu. “Efficient Attack Graph Analysis through Approximate Inference.” ACM Transactions on Privacy and Security, vol. 20(3), pp. 1-30, 2017.

Attack graphs provide compact representations of the attack paths an attacker can follow to compromise network resources from the analysis of network vulnerabilities and topology. These representations are a powerful tool for security risk assessment. Bayesian inference on attack graphs enables the estimation of the risk of compromise to the system’s components given their vulnerabilities and interconnections and accounts for multi-step attacks spreading through the system. While static analysis considers the risk posture at rest, dynamic analysis also accounts for evidence of compromise, for example, from Security Information and Event Management software or forensic investigation. However, in this context, exact Bayesian inference techniques do not scale well. In this article, we show how Loopy Belief Propagation—an approximate inference technique—can be applied to attack graphs and that it scales linearly in the number of nodes for both static and dynamic analysis, making such analyses viable for larger networks. We experiment with different topologies and network clustering on synthetic Bayesian attack graphs with thousands of nodes to show that the algorithm’s accuracy is acceptable and that it converges to a stable solution. We compare sequential and parallel versions of Loopy Belief Propagation with exact inference techniques for both static and dynamic analysis, showing the advantages and gains of approximate inference techniques when scaling to larger attack graphs.

Exact Inference Techniques for the Analysis of Bayesian Attack Graphs

Luis Muñoz-González, Daniele Sgandurra, Martín Barrere, and Emil C. Lupu. “Exact Inference Techniques for the Analysis of Bayesian Attack Graphs.” IEEE Transactions on Dependable and Secure Computing (TDSC), 16(2), pp. 231-234, 2019.

Attack graphs are a powerful tool for security risk assessment by analysing network vulnerabilities and the paths attackers can use to compromise network resources. The uncertainty about the attacker’s behaviour makes Bayesian networks suitable to model attack graphs to perform static and dynamic analysis. Previous approaches have focused on the formalization of attack graphs into a Bayesian model rather than proposing mechanisms for their analysis. In this paper we propose to use efficient algorithms to make exact inference in Bayesian attack graphs, enabling the static and dynamic network risk assessments. To support the validity of our approach we have performed an extensive experimental evaluation on synthetic Bayesian attack graphs with different topologies, showing the computational advantages in terms of time and memory use of the proposed techniques when compared to existing approaches.

Paper

SECRIS: Security Risk Assessment of IoT Environments with Attack Graph Models

IoT environments are vulnerable: many devices can be accessed physically and are not designed with security in mind. It is often impractical to patch all the vulnerabilities or to eliminate all possible threats. Unlike more traditional computing systems IoT environments bring together the physical, human and cyber aspects of a system. Each can be used to compromise the other and each can contribute towards monitoring and protecting the other.

Given the complexity of possible attacks, techniques for identifying and assessing the security risk are needed. In traditional networked environments attack graphs have been proven as a powerful tool for representing the different paths through which a system can be compromised. In this project we propose to design a new generation of attack graph models capable of describing the attack surface of modern IoT infrastructures for smart buildings. We are investigating new mechanisms to reduce the complexity of the attack graph representations and efficient algorithms for their analysis.

Exact Inference Techniques for the Dynamic Analysis of Attack Graphs

Attack graphs are a powerful tool for security risk assessment by analysing network vulnerabilities and the paths attackers can use to compromise valuable network resources. The uncertainty about the attacker’s behaviour and capabilities make Bayesian networks suitable to model attack graphs to perform static and dynamic analysis. Previous approaches have focused on the formalization of traditional attack graphs into a Bayesian model rather than proposing mechanisms for their analysis. In this paper we propose to use efficient algorithms to make exact inference in Bayesian attack graphs, enabling the static and dynamic network risk assessments. To support the validity of our proposed approach we have performed an extensive experimental evaluation on synthetic Bayesian attack graphs with different topologies, showing the computational advantages in terms of time and memory use of the proposed techniques when compared to existing approaches.

Luis Muñoz-González, Daniele Sgandurra, Martín Barrere, and Emil C. Lupu: Exact Inference Techniques for the Dynamic Analysis of Attack Graphs. arXiv preprint: arXiv:1510.02427. October, 2015.