A Decimal Coded Genetic Algorithm Recommender for P2P Systems

Research has witnessed a wide range of trust-based recommendation systems especially for P2P networks considering the open nature of networks. The recommendation systems have been devised with parameters, factors, and techniques to mitigate attacks on the recommendations. Though the ground functionalities are the same (trust management, reputation querying, recommendation filtering, and aggregation of fair recommendations) each of them have come up with different models which are unique in their own way. An important thread of research focuses on trust models for P2P systems based on the rule-based anomaly detection using genetic algorithm, evolving recommendations using genetic programming, etc. While, attempts have been made using binary coded GA, a decimal coded GA has not received attention so far. A decimal coded GA consumes less memory space and it gives accurate results. Our work focuses on evolving a self-organizing trust model for P2P systems using decimal coded genetic algorithm for mitigating service-based and recommendation-based attacks. A detailed discussion on responsiveness of P2P trust models to particularly malicious peer behavior, brings out the feasibility of the proposed model.