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An Effective Intrusion Detection Model for Dynamic Topological Channel Behavior Using Dense Node Behavior
R.M. Chamundeeswari and P Sumathi

Mobile Ad-hoc Network (MANET) with open medium and dynamic topological structure leads to different vulnerable attacks increasing the most challenging task in detecting intrusion detection model. Most of the existing Intrusion detection model in MANET provides security depending on the accessible resources while have trouble in dealing with intrusion detection model for dynamic topological channel behavior. In addition, the intrusion detection and response system in MANET does not address for dynamically changing topological zone. Due to the dynamic change involved in the topological structure, node location information is not sufficient for analyzing the behavior. Therefore, we are persuaded to design a new intrusion detection model which involves new detection architecture to efficiently detect the abnormalities in mobile ad hoc networks based on the knowledge of channel behavior. The research work is mainly concentrated on obtaining the perfect channel knowledge in mobile ad-hoc network model with accurate node location information. To improve the detection accuracy on dynamically changing topology, Prediction and Supportive architecture using Channel Prediction (PS-CP) is proposed in this paper. The PS-CP architecture deals with perfecting the channel knowledge and achieves higher security in MANET. First, a Channel Prediction scheme is employed that provides an extensive historic knowledge of mobile route path to easily predict and effortlessly perfect the dynamic topological channel. Then, an Arbitrary Bernoulli Matrix is employed to obtain relation between initial node point and dynamic network node changing topological structure aiming at improving intrusion detection accuracy. Finally, Dense Node Behavior model analyzes the behavior of dynamic topological control based on the located node information for minimizing the false positive rate. In addition, different location point are identified using PS-CP architecture to improve the intrusion detection rate thus enhances the false negative rate. Simulation results demonstrate that the proposed intrusion detection achieves efficient amount of predicted class positive rate, detection rate, detection accuracy, false positive rate and mean route lifetime in mobile ad-hoc network.

Keywords: Channel prediction, dynamic topological structure, mathematical node behavior, Bernoulli matrix, mobile ad-hoc network

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