Risk assessment and attack graph generation for collaborative infrastructures: a survey

Lever, Kirsty E; Kifayat, Kashif

doi:10.1504/ijccbs.2016.079081

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…The systematic literature review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) methodology [20]. The research question that guided the search was: "What is the state of the art of infrastructure risks in smart cities?…”

Section: Systematic Literature Surveymentioning

confidence: 99%

Disaster Management in Smart Cities

et al. 2021

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The smart city concept, in which data from different systems are available, contains a multitude of critical infrastructures. This data availability opens new research opportunities in the study of the interdependency between those critical infrastructures and cascading effects solutions and focuses on the smart city as a network of critical infrastructures. This paper proposes an integrated resilience system linking interconnected critical infrastructures in a smart city to improve disaster resilience. A data-driven approach is considered, using artificial intelligence and methods to minimize cascading effects and the destruction of failing critical infrastructures and their components (at a city level). The proposed approach allows rapid recovery of infrastructures’ service performance levels after disasters while keeping the coverage of the assessment of risks, prevention, detection, response, and mitigation of consequences. The proposed approach has the originality and the practical implication of providing a decision support system that handles the infrastructures that will support the city disaster management system—make the city prepare, adapt, absorb, respond, and recover from disasters by taking advantage of the interconnections between its various critical infrastructures to increase the overall resilience capacity. The city of Lisbon (Portugal) is used as a case to show the practical application of the approach.

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Section: Systematic Literature Surveymentioning

confidence: 99%

Disaster Management in Smart Cities

et al. 2021

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“…17 criteria have been identified and used for evaluating the quality of the algorithm. The selection was based on algorithm characteristics found on previous studies and current trends in risk management (Kaynar & Sivrikaya, 2016;Lever & Kifayat, 2016;Polatidis et al, 2018;Yi et al, 2013). The details, of the criteria can be found in appendix A, while the criteria are also presented briefly within table 3.…”

Section: Evaluation Criteriamentioning

confidence: 99%

From product recommendation to cyber-attack prediction: generating attack graphs and predicting future attacks

et al. 2018

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Modern information society depends on reliable functionality of information systems infrastructure, while at the same time the number of cyber-attacks has been increasing over the years and damages have been caused. Furthermore, graphs can be used to show paths than can be exploited by attackers to intrude into systems and gain unauthorized access through vulnerability exploitation. This paper presents a method that builds attack graphs using data supplied from the maritime supply chain infrastructure. The method delivers all possible paths that can be exploited to gain access. Then, a recommendation system is utilized to make predictions about future attack steps within the network. We show that recommender systems can be used in cyber defense by predicting attacks. The goal of this paper is to identify attack paths and show how a recommendation method can be used to classify future cyber-attacks in terms of risk management. The proposed method has been experimentally evaluated and validated, with the results showing that it is both practical and effective.

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Risk assessment and attack graph generation for collaborative infrastructures: a survey

Cited by 2 publications

References 25 publications

Disaster Management in Smart Cities

Disaster Management in Smart Cities

From product recommendation to cyber-attack prediction: generating attack graphs and predicting future attacks

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