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Mobile ad-hoc networks are widely used in the tactical battlefield, emergency search and rescue missions. They are also well used in civilian ad-hoc situations like conferences and classrooms due to the ease and speed in setting up such networks. Unlike traditional mobile wireless networks, ad hoc networks do not rely on any fixed infrastructure. Instead, hosts rely on each other to keep the network connected. The wireless adhoc networks are mostly vulnerable to security attacks because of its features of open medium, dynamic topology, lack of centralized management and node mobility. In this paper we proposed a means to inject black hole attack and wormhole with a protocol Ad-hoc On Demand Distance Vector (AODV) and conducted experiments using NS2 simulator. The results show that performance of network decreased in presence of attacks.

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Abstract-As MANETs change their topology dynamically, intrusion detection in these networks is a challenging task. These networks are more liable to the security attacks because of the properties such as node mobility, lack of concentration points where aggregated traffic can be analyzed, intermittent wireless communications and limited band width. We present a multiclass intrusion detection system that addresses these challenges. In this paper we propose a neural network method based on MLP (multi-layer perceptron) for detecting normal and attacked behavior of the system. The method was tested for Black Hole and Gray Hole attacks. We have implemented these attacks using NS2 simulator. The method successfully detected these attacks. We compared the results with KNN (K-Nearest Neighborhood) which is another classifier used for classification. Finally, Re sampling methods were also applied to assess the performance of classifier. This paper presents a graphical representation of the results.Index Terms-Intrusion detection system, Black Hole attack, Gray Hole attack, multi-layer perceptron, K-nearest neighborhood.