Hybrid and Distributed Intrusion Detection System using Artificial Immune System and Swarm Intelligence for Mobile Networks

The wireless mobile network is particularly vulnerable due to its features of open medium, dynamic changing topology, cooperative algorithms, lack of centralized monitoring and management point, and lack of a clear line of defense. The traditional way of protecting mobile networks with firewalls and encryption software is no longer sufficient and effective. New distributed intrusion detection schemes must be designed for wireless mesh mobile networks. In the first phase of the work a static simulation was completed for analyzing load balancing approaches in mobile network. In the second phase of the work we try to devise a method where we put a distributed IDS agent in each node of the wireless mobile network so that each node studies the packet flow in the corresponding mobile network and distributes it to the near by nodes in the mobile network. With this, the nodes will know the details of the packet flow in the entire mobile network using the swarm intelligence technique; thereby it tries to identify the intrusion that happens by various rule mining techniques. Here we implement hierarchical, extensible and flexible detection architecture to thwart such threats. Thus this system will help us detecting and tracking the intrusion at the early level itself with the use of hierarchical model. This will help us to reduce the false positives as well as false negatives, which will give more accuracy in detection and also increasing the quality of service in the mobile network. The overall load balancing characteristic of the network is hence improved.