Science & Engineering Research Support soCiety (SERSC)
Due to the growth of computer networks, network security has been proposed as a major challenge. Intrusion detection systems to ensure the safe processing and storage of data on the network have been developed. Considering that intrusion detection systems and anomaly clearly recognize malicious activity. Now-a-days, data mining based intrusion detection systems, security and more rapidly detect attacks. Therefore, in this paper, the authors use a combination of k-means clustering algorithm and are used supervised support vector machine algorithm to find the best line separator. This is leading to the separation of normal and attack traffic.