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Identifying and Mitigating Security Risks in Multi-Level Systems-of-Systems Environments

Lever, KE (2018) Identifying and Mitigating Security Risks in Multi-Level Systems-of-Systems Environments. Doctoral thesis, Liverpool John Moores University.

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In recent years, organisations, governments, and cities have taken advantage of the many benefits and automated processes Information and Communication Technology (ICT) offers, evolving their existing systems and infrastructures into highly connected and complex Systems-of-Systems (SoS). These infrastructures endeavour to increase robustness and offer some resilience against single points of failure. The Internet, Wireless Sensor Networks, the Internet of Things, critical infrastructures, the human body, etc., can all be broadly categorised as SoS, as they encompass a wide range of differing systems that collaborate to fulfil objectives that the distinct systems could not fulfil on their own. ICT constructed SoS face the same dangers, limitations, and challenges as those of traditional cyber based networks, and while monitoring the security of small networks can be difficult, the dynamic nature, size, and complexity of SoS makes securing these infrastructures more taxing. Solutions that attempt to identify risks, vulnerabilities, and model the topologies of SoS have failed to evolve at the same pace as SoS adoption. This has resulted in attacks against these infrastructures gaining prevalence, as unidentified vulnerabilities and exploits provide unguarded opportunities for attackers to exploit. In addition, the new collaborative relations introduce new cyber interdependencies, unforeseen cascading failures, and increase complexity. This thesis presents an innovative approach to identifying, mitigating risks, and securing SoS environments. Our security framework incorporates a number of novel techniques, which allows us to calculate the security level of the entire SoS infrastructure using vulnerability analysis, node property aspects, topology data, and other factors, and to improve and mitigate risks without adding additional resources into the SoS infrastructure. Other risk factors we examine include risks associated with different properties, and the likelihood of violating access control requirements. Extending the principals of the framework, we also apply the approach to multi-level SoS, in order to improve both SoS security and the overall robustness of the network. In addition, the identified risks, vulnerabilities, and interdependent links are modelled by extending network modelling and attack graph generation methods. The proposed SeCurity Risk Analysis and Mitigation Framework and principal techniques have been researched, developed, implemented, and then evaluated via numerous experiments and case studies. The subsequent results accomplished ascertain that the framework can successfully observe SoS and produce an accurate security level for the entire SoS in all instances, visualising identified vulnerabilities, interdependencies, high risk nodes, data access violations, and security grades in a series of reports and undirected graphs. The framework’s evolutionary approach to mitigating risks and the robustness function which can determine the appropriateness of the SoS, revealed promising results, with the framework and principal techniques identifying SoS topologies, and quantifying their associated security levels. Distinguishing SoS that are either optimally structured (in terms of communication security), or cannot be evolved as the applied processes would negatively impede the security and robustness of the SoS. Likewise, the framework is capable via evolvement methods of identifying SoS communication configurations that improve communication security and assure data as it traverses across an unsecure and unencrypted SoS. Reporting enhanced SoS configurations that mitigate risks in a series of undirected graphs and reports that visualise and detail the SoS topology and its vulnerabilities. These reported candidates and optimal solutions improve the security and SoS robustness, and will support the maintenance of acceptable and tolerable low centrality factors, should these recommended configurations be applied to the evaluated SoS infrastructure.

Item Type: Thesis (Doctoral)
Uncontrolled Keywords: Network Security; Optimisation; Risk Mitigation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Date Deposited: 12 Jun 2018 08:54
Last Modified: 08 Nov 2022 13:50
DOI or ID number: 10.24377/LJMU.t.00008707
Supervisors: Kifayat, K and Zhou, B
URI: https://researchonline.ljmu.ac.uk/id/eprint/8707
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