Cyber rupture incidents have increased severely during COVID-19 pandemic and maintain a cyclical trend skilled after. Data breach occurrence result in harsh financial loss and reputational damage to trade, government, healthcare and educational organizations. Compared to sufficient amount of cyber risk case in economic and IT system rule, seldom investigations of high-tech risk have been made in all-inclusive perspective, In order to fill this gap, we suggest a Bayesian generalized linear assorted model to analyze data rift incidents chronicle since 2001. Our model captures the dependency 'tween frequency and severity of high-tech losses, and the behavior of high-tech attacks on entities across time. Risk traits such as types of breach, types of institution, entity locations in chronicle, as well as time current effects are taken into concern when investigating breach repetitions. A statistical predicting model is generated under actuarial mathematics frame, accompanying flexible input usable such as location and arranging types. Predictions and implications of the proposed model in adventure risk management and cyber protection rate filing are discussed and pictorial. Our results show that both geological area and business type play significant duties in measuring cyber risks. The consequences of our predictive data provide numerical bills loss level that can be took advantage of by various kinds of arrangements and design their risk mitigation strategies.
Author(s) Details:
Meng Sun,
Simon Fraser University, BC, Canada.
Please see the link here: https://stm.bookpi.org/CTBEF-V5/article/view/10490
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