Abstract
This work proposes a simple and effective Intrusion Detection System (IDS) to classify different attacks in MANETs. IDS extracts four features for every traffic pattern and applies Support Vector Machine algorithm over them for the classification. Before applying the feature extraction, the input traffic pattern is subjected to pre-processing as it is composed of non-uniform features. IDS classifies the input traffic pattern into three classes; they are normal, blackhole and wormhole. Finally, this work analyses the feasibility of machine learning algorithms for the detection of security attacks in MANETs. For experimental validation, we have referred a self-created dataset which was acquired from the observations of blackhole and wormhole attacked node’s traffic patterns. Moreover, we have also validated the proposed method through NSL-KDD dataset.
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