| Title | Benchmarking Evolutionary Computation Approaches to Insider Threat Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Le, Duc C., Khanchi, Sara, Zincir-Heywood, A. Nur, Heywood, Malcolm I. |
| Conference Name | Proceedings of the Genetic and Evolutionary Computation Conference |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-5618-3 |
| Keywords | cyber-security, Human Behavior, insider threat, Insider Threat Detection, Metrics, pubcrawl, resilience |
| Abstract | Insider threat detection represents a challenging problem to companies and organizations where malicious actions are performed by authorized users. This is a highly skewed data problem, where the huge class imbalance makes the adaptation of learning algorithms to the real world context very difficult. In this work, applications of genetic programming (GP) and stream active learning are evaluated for insider threat detection. Linear GP with lexicase/multi-objective selection is employed to address the problem under a stationary data assumption. Moreover, streaming GP is employed to address the problem under a non-stationary data assumption. Experiments conducted on a publicly available corporate data set show the capability of the approaches in dealing with extreme class imbalance, stream learning and adaptation to the real world context. |
| URL | http://doi.acm.org/10.1145/3205455.3205612 |
| DOI | 10.1145/3205455.3205612 |
| Citation Key | le_benchmarking_2018 |
Benchmarking Evolutionary Computation Approaches to Insider Threat Detection