Network Intrusion Detection Using Hybrid Simplified Swarm Optimization Technique

--Network security risks grow tremendously in recent past, the attacks on computer networks have enhanced hugely and need economical network intrusion detection mechanisms. Data processing and machine-learning techniques are used for network intrusion detection throughout the past few years and have gained abundant quality. In this paper, we propose an intrusion detection mechanism based on simplified particle swarm optimization (SSO) is used to investigate the performance of various dimension reduction techniques along with a set of different classifiers including the proposed approach. SSO is used to find more appropriate set of attributes for classifying network intrusions, and also used as a classifier. In preprocessing step, we reduce the dimensions of the dataset by using various dimension reduction techniques, and then this reduced dataset is offered to the proposed hybrid SSO approach that further optimizes the dimensions of the data and finds an optimal set of features. SSO is an optimization method that has a strong global search capability and is used for dimension optimization. The analysis performed on standard KDD cup99 dataset which contain various kind of intrusion. The experimental results shows the worth of the proposed approach by using different performance metrics. Keywords-Swarm intelligence, Simplified Swarm Optimization, optimization, Data mining, Intrusion Detection.