A hybrid framework using explainable AI (XAI) in cyber-risk management for defence and recovery against phishing attacks
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Phishing 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.
Açıklama
Anahtar Kelimeler
Information Security, Explainable Ai, Cyber Insurance, Bivariate Distributions, Copula
Kaynak
Decision Support Systems
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
177
