Anomaly detection approach based on quantum-behaved partical swarm optimization
Wenbo Xu · Computer Engineering and Applications Journal · 2007
Differentiating the normal state and anomaly state is a difficult task in intrusion detection system,to solve the problem,an approach that applies Quantum-Behaved Partical Swarm Optimization(QPSO) to optimize parameters of membership functions in anomaly detection is presented.Parameters of membership functions are arranged into partical swarm,an optimal parameter-set could be derived by embedding fuzzy data mining in the process of evolution of partical,so normal state and anomaly state could be differentiated in the most extent,and the accuracy of anomaly detection is enhanced.Experiments prove the feasibility of the approach.