Developing an Autonomous Framework for Effective Detection of Intrusions

With the growing popularity of Internet, it becomes necessary to protect our systems from imminent security breaches. Apart from the traditional types of attacks, the systems are prone to more sophisticated attacks that originate from various malware systems. Latest developments in the area of intrusion detection systems (IDS) have brought tremendous improvement in detecting the attacks efficiently. But very limited work has been done in the field of autonomous intrusion detection systems. The increased speed and complexity of attacks during recent years show an acute necessity for a more intelligent and autonomous detection mechanism. The proposed IDS involves analyzing the activities of the system and detecting any suspicious behavior on the system. A hierarchical evolutionary model is used to build the autonomous intrusion detection system. A two-phase evolutionary-based method is used in order to detect the intrusions effectively. The first phase generates a list of non-dominated solutions that is used in the second phase for classifying the new packets as normal or intrusive. Results obtained demonstrate very promising results compared to the already existing multi-objective algorithms.