Anomaly Based Improved Network Intrusion Detection System Using Clustering Techniques

The detection of new threats has become a need for secured communication to provide complete data confidentiality. The network requires anomaly detection to shield from hurtful activities. There are various types of metaheuristic methods used for anomaly detection. In this paper, a new approach is proposed for network anomaly detection using multi-start metaheuristic method and enhancement in clustering algorithms. The main stages involved in the proposed approach are: preprocessing, clustering, training dataset selection and the performance evaluation based on training and testing dataset to detect anomalies. The performance of two clustering algorithms, i.e. K-means and expectation maximization (EM) is compared using detection accuracy, false positive rate, and detector generation time. The experimental results are based on NSL-KDD dataset. The results show that the EM clustering performs better than K-means clustering algorithm.