Secure cyber incident analytics framework using Monte Carlo simulations for financial cybersecurity insurance in cloud computing

Abstract

Summary The remarkable increasing demands of mitigating losses from cyber incidents for financial firms have been driving the rapid development of the Cybersecurity Insurance (CI). The implementations of CI have covered a variety of aspects in cyber incidents, from hacking to frauds. However, CI is still at its exploring stage so that there are a number of dimensions that are uncovered by the current applications. The cyber attack on critical infrastructure is one of the serious issues that prevents the expansions of CI. This paper addresses CI implementations focusing on cloud‐based service offerings and proposes a secure cyber incident analytics framework using big data, named as Cost‐Aware Hierarchical Cyber Incident Analytics (CA‐HCIA). The approach is designed for matching different cyber risk scenarios, which uses repository data. We use Monte Carlo simulations for extracting the incident features based on the training datasets. The main algorithms in CA‐HCIA include Monte Carlo Cyber Feature Extraction (MC2FE) and Optimal Cost Balance (OCA) Algorithms. Our experimental evaluation has provided the theoretical proof of the adoptability and feasibility. Results show that our proposal improves the cost of existing techniques in 7.98% and 15.39%. Copyright © 2016 John Wiley & Sons, Ltd.

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“…Semantic techniques create a knowledge representation graph. The study [35] was further enhanced [104] and applied Monte Carlo simulations to analyze the data more efficiently considering the security framework. Furthermore, several studies previously focused on physical locations for risk reduction.…”

“…Furthermore, several studies previously focused on physical locations for risk reduction. Gai et al [104] implemented Radio Frequency ID (RFID) based applications to increase security. The approach basically takes geographical information under consideration that is related to data.…”

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