Intrusion Detection System using decision tree algorithm

Intrusion Detection System (IDS) is the most powerful system that can handle the intrusions of the computer environments by triggering alerts to make the analysts take actions to stop this intrusion. IDS's are based on the belief that an intruder's behavior will be noticeably different from that of a legitimate user. A variety of intrusion detection systems (IDS) have been employed for protecting computers and networks from malicious attacks by using traditional statistical methods to new data mining approaches in last decades. However, today's commercially available intrusion detection systems are signature-based that are not capable of detecting unknown attacks. In this paper we analyze a classification model for misuse and anomaly attack detection using decision tree algorithm.