Detection of slanders through Euclidean distance similarity assessment for securing e-commerce agents in P2P decentralised electronic communities

摘要: Mobile Agents migrate autonomously through the network. During execution, agents are controlled by hosts. Hence, need arises to protect agent against malicious intentions of Proposed model 'MRep' computes reputation Destination Host DH with which wishes do transaction. Hosts as recommenders may give fair/unfair recommendations for DH. Our approach calculates Euclidean Distance ED between Source pre-trusted and recommender. If lies in similarity range, recommendation recommender is fair else, it unfair. dissimilarity correlation calculated estimate strong, medium or weak similarity. High area under curve proves a high association receiver operating characteristic ROC curves. Results show that possess 80% accuracy Final Reputation score before slandering while only 50% achieved after slandering. This concludes existence slanders lowers performance model.