Tracking and identification of suspicious and abnormal behaviors using supervised machine learning technique

The explosion in popularity of open systems interconnected via the pubic network and private network has made computer security an issue of increasing concern. In networks there is a major issue to secure the public or private network from suspicious behaviors. This is because each network is made of users, services and computers with a specific behavior that is then reflected in the generated network traffic to detect abnormal user suspicious detection system is used. This paper presents a supervised learning technique which is used to detect various suspicions and abnormal behaviors in the public or private networks. Our technique also detect various behaviors of these hosts using supervised learning methodology.