Immune anomaly detection enhanced with evolutionary paradigms

The paper presents an approach based on principles of immune systems to the anomaly detection problem. Flexibility and efficiency of the anomaly detection system are achieved by building a model of network behavior based on the selfnonself space paradigm. Covering both self and nonself spaces by hyperrectangular structures is proposed. Structures corresponding to self-space are built using a training set from this space. Hyperrectangular detectors covering nonself space are created using niching genetic algorithm. A coevolutionary algorithm is proposed to enhance this process. Results of experiments show a high quality of intrusion detection, which outperform the quality of recently proposed approach based on hypersphere representation of self-space. Categories and Subject Descriptors: I.2.8 [Problem Solving, Control Methods, and Search]: Heuristic methods General Terms: Algorithms, Experimentation.

• Type: Conference
• Year: 2006
• Where: GECCO
• Authors: Marek Ostaszewski, Franciszek Seredynski, Pascal Bouvry
• Keywords: Anomaly Detection, GECCO 2006, Niching Genetic Algorithm, Optimization, Selfnonself Space Paradigm