A Mobile Ad-hoc network (MANET) is a multi-hop wireless network where nodes communicate with each other without any pre-deployed infrastructure. An attack is an attempt to bypass the security controls on a computer. The attack may alter, release, or deny data. Intrusion Detection System is a process of monitoring activities in a system, which can be a computer or network system. The mechanism by which this is achieved is called an intrusion detection system. Once an IDS determines that an unusual activity occurs, it then generates an alarm to alert the security administrator. This paper proposes an artificial neural network (ANN) method to find misuse detection in MANETs. Proposed method detects the attacks, corresponding to known pattern at the mobile nodes. At each mobile node whether the known attack is present or not is detected by comparing it with known patterns. These patterns are trained to ANN. Back propagation algorithm is used to train the network. To test the operative effectiveness of the proposed system, the proposed detection method is analyzed in terms of mean square error, number of iterations, computation path time taken to reach required accuracy, and change in learning rate parameter for various network scenarios.

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Mobile ad hoc network Intrusion detection system Artificial neural network Back propagation algorithm