A Linear Genetic Programming Approach to Intrusion Detection

Abstract. Page-based Linear Genetic Programming (GP) is proposed and implemented with two-layer Subset Selection to address a two-class intrusion detection classification problem as defined by the KDD-99 benchmark dataset. By careful adjustment of the relationship between subset layers, over fitting by individuals to specific subsets is avoided. Moreover, efficient training on a dataset of 500,000 patterns is demonstrated. Unlike the current approaches to this benchmark, the learning algorithm is also responsible for deriving useful temporal features. Following evolution, decoding of a GP individual demonstrates that the solution is unique and comparative to hand coded solutions found by experts.

• Type: Conference
• Year: 2003
• Where: GECCO
• Authors: Dong Song, Malcolm I. Heywood, A. Nur Zincir-Heywood
• Keywords: GECCO 2003, GP Individual Demonstrates, KDD-99 Benchmark Dataset, Two-layer Subset Selection