Multi-Objective Optimization of Intrusion Detection Systems for Wireless Sensor Networks
| Authors | |
|---|---|
| Year of publication | 2013 |
| Type | Article in Proceedings |
| Conference | Advances in Artificial Life, ECAL 2013, Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems |
| MU Faculty or unit | |
| Citation | |
| Web | http://mitpress.mit.edu/sites/default/files/titles/content/ecal13/ch082.html |
| Doi | http://dx.doi.org/10.7551/978-0-262-31709-2-ch082 |
| Field | Informatics |
| Keywords | Evolutionary algorithm; Multi-objective evolutionary algorithm; Optimization; Wireless sensor network; Intrusion detection system |
| Description | Intrusion detection is an essential mechanism to protect wireless sensor networks against internal attacks that are relatively easy and not expensive to mount in these networks. Recently, we proposed, implemented and tested a framework that helps a network operator to find a trade-off between detection accuracy and usage of resources that are usually highly constrained in wireless sensor networks. We used a single-objective optimization evolutionary algorithm for this purpose. This approach, however, has its limitations. In order to eliminate them, we show benefits of multi-objective evolutionary algorithms for intrusion detection parametrization and examine two multi-objective evolutionary algorithms (NSGA-II and SPEA2). Our examination focuses on the impact of an evolutionary algorithm (and its parameters) on the optimality of found solutions, the speed of convergence and the number of evaluations. |
| Related projects: |