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

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

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