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Insider Threat Detection: Machine Learning Way

  • Mehul S. Raval
  • Ratnik Gandhi
  • Sanjay Chaudhary
Chapter
Part of the Advances in Information Security book series (ADIS, volume 72)

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.

Notes

Acknowledgments

We would like to thank Ativ Joshi and Pratik Paladia for helping with the experiments. We also express gratitude to unknown reviewers for insightful comments in improving the quality of this chapter.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Mehul S. Raval
    • 1
  • Ratnik Gandhi
    • 1
  • Sanjay Chaudhary
    • 1
  1. 1.School of Engineering and Applied ScienceAhmedabad UniversityAhmedabadIndia

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