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International Conference on Evolution in Engineering & Management (ICEEM-2015), 30-31, January 2015

Malicious Detection in MANET Using Enhancement Adaptive Acknowledgement

C. Rajan, N. Shanthi and S. Geetha


The migration to wireless network from wired network has been a global trend for last few decades. Uniqueness of mobility and scalability brought by wireless network made it possible in many applications. Among all the current wireless networks, Mobile Ad hoc Network(MANETƑ is one of the most important and sole applications. On the divergent to traditional network architecture, MANET does not require any fixed network infrastructure; every single node works as both a transmitter and a receiver. Nodes can communicate directly with each other when they are both within the same range of communication. Otherwise, they rely on their neighbors to transmit messages. The self-configuring ability of nodes in MANET made it popular among critical mission applications like military use or emergency healing. However, the open medium and wide circulation of nodes make MANET at risk to malicious attackers. In this level, it is crucial to develop efficient intrusion-detection mechanisms to protect MANET from attacks. With the improvements of the technology and cut in hardware expenses, we are witnessing a current trend of increasing MANETs into industrial applications. Tomake regulate such trend, we strongly believe that it is vital to address its potential security issues. In this paper, we propose and implement a new intrusion-detection system named Enhanced Adaptive Acknowledgment (EAACK) specially planned for MANETs. Compared to existing approaches, EAACK demonstrates higher malicious-behavior-detection rates in certain circumstances while does not greatly affect the network performances.

Keywords: Malicious Node Detection, Intrusion Detection, Misbehavior Node Detecting Algorithm

Conference Name: Third National Conference on Networking and Communication Systems

Conference Date: 13, February 2015

Pages: 38-42

Paper ID: ncs10

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