IMPROVED PREDICTION STRATEGY USING PARTICLE SWARM OPTIMIZATION BASED ARTIFICIAL NEURAL NETWORK (IPS-ANN) CLASSIFIER FOR MALICIOUS NODE DETECTION FOR HYBRID P2P NETWORKS
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Abstract
Mining data from the hybrid P2P networks is an ever demanding task for detecting malicious behavior of nodes. This research work aims to propose an improved prediction strategy using particle swarm optimization based artificial neural network (IPS-ANN) classifier. From the extensive literature study it is identified that only very few literatures are there for detecting malicious node activities. The simulation are carried out using MATLAB by configuring 1000 nodes with three different attack scenarios namely collusion attack, Sybil attacks and file polluter attacks. Detection accuracy, false positive rate and false negative rate are taken as the performance metrics for evaluating the efficiency of the proposed classifier and also compared with the existing mechanisms. Results proved that the proposed IPS-ANN classifier better than that of PeerMate , SMART , Outlier mining mechanisms.
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