Intrusion Detection in MANET using Neural Networks and ZSBT

Mobile ad-hoc network is a collection of mobile nodes that organize themselves into a network without any predefined infrastructure. The characteristics of MANET are dynamic topology; bandwidth and energy constrained and limited physical security. Due to the dynamic nature of the network, these networks can be easily vulnerable to attacks. Many type of attacks can threat the MANET and the classification of attacks are Black hole, Routing loops, Network partition, Selfishness, Sleep deprivation and Denial of Service. The MANET is mainly applied in military environments, personal area networking and emergency operations. This work provides a technique to improve the security level of MANET. The mechanism of intrusion detection is designed in MANET on the basis of artificial neural networks (ANNs) and ZoneSampling Based Trace back algorithm (ZSBT) for detecting DOS attacks. Intrusion detection system is a type of security management system for computers and networks. The prevention mechanisms are thwarted by the ability of attackers to forge, or spoof, the source addresses in IP packets. By employing IP spoofing, attackers can evade from detection. But with the help of IDPF, the attacker node can be identified easily and it also has the ability to limit the IP spoofing capability. Artificial neural network and ZSBT modeling uses simulated MANET environments for detecting nodes under DOS attack effectively. But the ZSBT method works well for detecting DoS attacks in the network when compared with ANN methods.