Review on speedup and accurate intrusion detection system by using MSPSO and data mining technology

Intrusion detection is the act of detecting unwanted traffic on a network or a device. An IDS can be a piece of installed software or a physical appliance that monitors network traffic in order to detect unwanted activity and events such as illegal and malicious traffic, traffic that violates security policy, and traffic that violates acceptable use policies. However, Intrusion detection systems face a number of challenges. One of the important challenges is that, the input data to be classified is in a high dimension feature space. In this paper, we are trying to present MSPSO-DT intrusion detection system. Where, Multi Swam Particle Swarm Optimization (MSPSO) is used as a feature selection algorithm to maximize the C4.5 Decision Tree classifier detection accuracy and minimize the timing speed. To evaluate the performance of the presented MSPSO-DT IDS we are trying to use several experiments on NSL-KDD benchmarked network intrusion detection dataset.