Multi-classification approach for detecting network attacks

Intrusion Detection System (IDS) has increasingly become a crucial issue for computer and network systems. Intrusion poses a serious security risk in a network environment. The ever growing new intrusion types pose a serious problem for their detection. The acceptability and usability of Intrusion Detection Systems get seriously affected with the data in network traffic. A large number of false alarms mean a lot in terms of the acceptability of Intrusion Detection Systems[1].In this paper we consider the dataset with multi classes and propose the classification for each type of attacks in a separate layer. In this work, a multi-classification approach for detecting network attacks is designed and developed to achieve high efficiency and improve the detection and classification rate accuracy [6].