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• W2896940265 abstract "The chapter aims to cover and analyse contributions from machine learning to detect an insider threat. It presents various launch mechanisms and details impact of an insider attack on various sectors. Presenting state-of-the-art for detecting insider threat based on psychology, criminology and game theory, the chapter also covers case studies showing use of Machine Learning for anomaly detection. In real life, malicious events are low in number. The chapter will showcase detection of such a low occurring anomaly from a large dataset accurately. The chapter specifically focuses on USB device insertion or removal event and apply linear regression followed by Cook’s and Mahalanobis distance to identify malicious activities of the user. Subsequently, it applies Neural Network and Support Vector Machine to login activities of a user to successfully demonstrates detection of an anomaly behaviour. It concludes discussing future directions that uses combination of methods from natural language processing, behavioural analysis, sentiment analysis, and machine learning for insider threat detection." @default.
• W2896940265 created "2018-10-26" @default.
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• W2896940265 date "2018-01-01" @default.
• W2896940265 modified "2023-09-26" @default.
• W2896940265 title "Insider Threat Detection: Machine Learning Way" @default.
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• W2896940265 doi "https://doi.org/10.1007/978-3-319-97643-3_2" @default.
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