Are cyber-blackouts in service networks likely?: Implications for Aggregate Cyber Risk Management

@TechReport{UCAM-CL-TR-926, author = {Pal, Ranjan and Psounis, Konstantinos and Kumar, Abhishek and Crowcroft, Jon and Hui, Pan and Golubchik, Leana and Kelly, John and Chatterjee, Aritra and Tarkoma, Sasu}, title = {{Are cyber-blackouts in service networks likely?: implications for cyber risk management}}, year = 2018, month = oct, url = {https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-926.pdf}, institution = {University of Cambridge, Computer Laboratory}, number = {UCAM-CL-TR-926} }Service liability interconnections among networked IT and IoT driven service organizations create potential channels for cascading service disruptions due to modern cybercrimes such as DDoS, APT, and ransomware attacks. The very recent Mirai DDoS and WannaCry ransomware attacks serve as famous examples of cyber-incidents that have caused catastrophic service disruptions worth billions of dollars across organizations around the globe. A natural question that arises in this context is “what is the likelihood of a cyber-blackout?”, where the latter term is defined as: “the probability that all (or a major subset of) organizations in a service chain become dysfunctional in a certain manner due to a cyber-attack at some or all points in the chain”. The answer to this question has major implications to risk management businesses such as cyber-insurance when it comes to designing policies by risk-averse insurers for providing coverage to clients in the aftermath of such catastrophic network events. In this paper, we investigate this question in general as a function of service chain networks and different loss distribution types. We show somewhat surprisingly (and discuss potential practical implications) that following a cyber-attack, the probability of a cyber-blackout and the increase in total service-related monetary losses across all organizations, due to the effect of (a) network interconnections, and (b) a wide range of loss distributions, are mostly very small, regardless of the network structure – the primary rationale behind the results being attributed to degrees of heterogeneity in wealth base among organizations, and Increasing Failure Rate (IFR) property of loss distributions

When Are Cyber Blackouts in Modern Service Networks Likely?: A Network Oblivious Theory on Cyber (Re)Insurance Feasibility

Service liability interconnections among globally networked IT- and IoT-driven service organizations create potential channels for cascading service disruptions worth billions of dollars, due to modern cyber-crimes such as DDoS, APT, and ransomware attacks. A natural question that arises in this context is: What is the likelihood of a cyber-blackout?, where the latter term is defined as the probability that all (or a major subset of) organizations in a service chain become dysfunctional in a certain manner due to a cyber-attack at some or all points in the chain. The answer to this question has major implications to risk management businesses such as cyber-insurance when it comes to designing policies by risk-averse insurers for providing coverage to clients in the aftermath of such catastrophic network events. In this article, we investigate this question in general as a function of service chain networks and different cyber-loss distribution types. We show somewhat surprisingly (and discuss the potential practical implications) that, following a cyber-attack, the effect of (a) a network interconnection topology and (b) a wide range of loss distributions on the probability of a cyber-blackout and the increase in total service-related monetary losses across all organizations are mostly very small. The primary rationale behind these results are attributed to degrees of heterogeneity in the revenue base among organizations and the Increasing Failure Rate property of popular (i.i.d/non-i.i.d) loss distributions, i.e., log-concave cyber-loss distributions. The result will enable risk-averse cyber-riskmanagers to safely infer the impact of cyber-attacks in a worst-case network and distribution oblivious setting.Peer reviewe

Computation in Complex Networks

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin