A
Arunabha Mukhopadhyay
Researcher at Indian Institute of Management Lucknow
Publications - 42
Citations - 365
Arunabha Mukhopadhyay is an academic researcher from Indian Institute of Management Lucknow. The author has contributed to research in topics: Computer science & Risk management. The author has an hindex of 8, co-authored 33 publications receiving 279 citations. Previous affiliations of Arunabha Mukhopadhyay include Indian Institute of Management Ahmedabad & Indian Institute of Management Calcutta.
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Journal ArticleDOI
Cyber-risk decision models: To insure IT or not?
TL;DR: A Copula-aided Bayesian Belief Network for cyber-vulnerability assessment (C-VA), and expected loss computation, and a utility based preferential pricing (UBPP) model to help firms decide on the utility of cyber-insurance products and to what extent they can use them are proposed.
Proceedings ArticleDOI
e-Risk Management with Insurance: A Framework Using Copula Aided Bayesian Belief Networks
TL;DR: This work develops a framework, based on copula aided Bayesian Belief Network (BBN) model, to quantify the risk associated with online business transactions, arising out of a security breach, and thereby help in designing e-insurance products.
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Stock Market Response to Information Security Breach: A Study Using Firm and Attack Characteristics
TL;DR: Modelling the cumulative abnormal response of the stock market to publicly announced breaches on a sample of Indian and US firms revealed that Denial of Service attacks on e-commerce companies and information theft attacks on BFSI companies generated significantly negative CAR.
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Cyber Risk Assessment and Mitigation (CRAM) Framework Using Logit and Probit Models for Cyber Insurance
TL;DR: This paper proposes a cyber-risk assessment and mitigation (CRAM) framework to estimate the probability of an attack using generalized linear models (GLM), namely logit and probit, and validate the same using CSI–FBI time series data.
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G-RAM framework for software risk assessment and mitigation strategies in organisations
TL;DR: This is the first study in IT security to examine and forecast volatility, and further design risk-optimal software portfolios, and links software risk assessment to IT governance and strategic business objectives.