A hybrid framework using explainable AI (XAI) in cyber-risk management for defence and recovery against phishing attacks

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dc.authoridDelen, Dursun/0000-0001-8857-5148
dc.authorwosidKUMAR, AJAY/KIC-8060-2024
dc.authorwosidMukhopadhyay, Arunabha/G-9434-2016
dc.authorwosidDelen, Dursun/AGA-9892-2022
dc.contributor.authorBiswas, Baidyanath
dc.contributor.authorMukhopadhyay, Arunabha
dc.contributor.authorKumar, Ajay
dc.contributor.authorDelen, Dursun
dc.date.accessioned2024-05-19T14:42:43Z
dc.date.available2024-05-19T14:42:43Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractPhishing and social engineering contribute to various cyber incidents such as data breaches and ransomware attacks, financial frauds, and denial of service attacks. Often, phishers discuss these attack vectors in dark forums. Further, the probability of phishing attacks and the subsequent loss suffered by the firm are highly correlated. In this context, we propose a hybrid framework using explainable AI techniques to assess cyber-risks generated from correlated phishing attacks. The first phase computes the probability of expert phishers within a community of similar attackers with varying expertise. The second phase calculates the probability of phishing attacks upon a firm even after it has invested in IT security and adopted regulatory steps. The third phase categorises phishing and genuine URLs using various machine-learning-based classifiers. Next, it estimates the joint distribution of phishing attacks using an exponential-beta distribution and quantifies the expected loss using Archimedean Copula. Finally, we offer recommendations for firms through the computation of optimal investments in cyberinsurance versus IT security. First, based on the risk attitude of a firm, it can use this explainable-AI (XAI) framework to optimally invest in building security into its enterprise architecture and plan for cyber-risk mitigation strategies. Second, we identify a long-tail phenomenon demonstrated by the losses suffered during most cyber-attacks, which are not one-off incidents and are correlated. Third, contrary to the belief that cyberinsurance markets are ineffective, it can guide financial firms to design realistic cyber-insurance products.en_US
dc.identifier.doi10.1016/j.dss.2023.114102
dc.identifier.issn0167-9236
dc.identifier.issn1873-5797
dc.identifier.scopus2-s2.0-85174589137en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.dss.2023.114102
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5275
dc.identifier.volume177en_US
dc.identifier.wosWOS:001141657800001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofDecision Support Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectInformation Securityen_US
dc.subjectExplainable Aien_US
dc.subjectCyber Insuranceen_US
dc.subjectBivariate Distributionsen_US
dc.subjectCopulaen_US
dc.titleA hybrid framework using explainable AI (XAI) in cyber-risk management for defence and recovery against phishing attacksen_US
dc.typeArticleen_US

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