Abstract
Adhoc network is highly susceptible for intrusion attacks due to the simplified accesscontrol and compacted network stack. Malicious node recognition in Mobile adhocnetwork (MANET) is challengeable due to nodes mobility and limited coverageof nodes. Thus, link may keep fluctuating throughout the communication period.In this paper, deep analytic model is made for extracting attacker node behaviorsfrom networking point of view. Attributed such as link durations, re-healing timeand number of received packets (by attacker) was the main features of this work.Later, deep learning paradigm is integrated to perform attacker node recognition. Dataobtained from network analytical model is used to train three different models namelyFeed forward neural network (FFNN), Cascade backpropagation neural network(CBPNN) and Convolutional neural network (CNN). Attacker node recognitionaccuracy of 85.5
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