Mitigating insider threats using bio-inspired models.

Abstract

Insider Threat has become a huge information security issue that governments and organizations must face. The implementation of security policies and procedures may not be enough to protect organizational assets. Even with the evolution of information and network security technology, the insider threat problem is on the rise and many researchers are approaching the problem with various methods, in order to develop a model that will help organizations to reduce their exposure to the threat and prevent damage to their assets. In this M.Sc. dissertation we approach the insider threat problem and attempt to mitigate it, by developing a machine learning model based on bio-inspired computing. The model was developed by using an existing unsupervised learning algorithm for anomaly detection and we fitted the model to a synthetic dataset to detect outliers. We explored swarm intelligence algorithms and their performance on feature selection optimization for improving the performance of the machine learning model. The results showed that swarm intelligence algorithms perform well on feature selection optimization and the generated near-optimal subset of features that has similar performance with the original one.