MSPSO based Improved Intrusion Detection system by using Classifier

Intrusion detection is the act of detecting unwanted traffic on a network or a device. An IDS can be a piece of introduced programming or a physical appliance that monitors network traffic in order to detect unwanted activity and events such as illegal and malicious traffic, traffic that abuses security strategy, and traffic that damages acceptable use policies. The exiting PSO algorithms are analyzed deeply, a multi-swarm PSO (MSPSO) is studied. The whole swarm is divided into two sub-swarms randomly the first particle group obeys the standard PSO principle to search the ideal result, the second searches randomly inner neighborhood of the optimal result and does not care about the optimal result but flies freely according to themselves velocities and positions. So the algorithm improves its worldwide searching space, enriches particles’ diversity in order to let particles jump out local optimization points. In this paper, we are trying to present Multi Swam Particle Swarm Optimization (MSPSO) intrusion detection system. Multi Swam Particle Swarm Optimization (MSPSO) is used as classifier detection accuracy and minimizes the timing speed by using NSL-KDD.