Cyber Risk Quantification and Mitigation Framework for Health care using Machine Learning

Pal, Shounak and Mukhopadhyay, Arunabha (2018) Cyber Risk Quantification and Mitigation Framework for Health care using Machine Learning. American Conference on Information Systems.

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Compromise of confidentiality, integrity and availability (C-I-A) of patient data may lead to tangible as well as intangible losses to a healthcare organization including loss of reputation, compensation, restoring and improving security. Prior work has already mentioned the effect of immediate environment in fostering criminal intent. However, significant literature is absent with regards to socio-economic factors. Our work will help in complete risk management using the CRQ-CRM framework. The first module (CRQ) includes classification of attack-type using attack patterns and socio-economic factors. Our second module (CRM) takes the prediction accuracy, as an input and computes the expected loss and the consequent impact-probability matrix. Using the matrix, we could prescribe further course of action to improve on the prediction model. Our work will aid managers towards effective risk management and further open new avenues of research which includes state-level factors.

Item Type: Article
Subjects: R Medicine > R Medicine (General)
T Technology > T Technology (General)
T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Industrial Engineering and Informatics > Information System
Depositing User: staff repository 2
Date Deposited: 12 Sep 2018 14:37
Last Modified: 12 Sep 2018 14:37

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