- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
With the increases of P2P applications and their users, the malicious attacks also increased significantly, which negatively impacts on the availability of the P2P networks and their users’ experience. This paper presents an outlier mining-based malicious node detection model for hybrid P2P networks. We first extract the local nodes’ frequent patterns from the nodes’ behavior patterns in subnets using the frequent behavior pattern mining approach, and then we produce and update the nodes’ global frequent behavior patterns by incrementally propagating and aggregating the local frequent behavior patterns. Finally, we identify outliers (i.e. the malicious nodes) using the local frequent behavior patterns and the global frequent behavior patterns. We also discuss how to recognize the different types of malicious nodes from outliers. Simulation results show that our strategy could detect malicious nodes with low false positive rate and low false negative rate.
In this paper, we mainly discussed how to detect malicious peers using outlier mining approach in hybrid P2P networks. We first presented several definitions, and described a peer’s behavior patterns based on the peer’s interaction data. Then, we detailed the local frequent behavior pattern mining process and the global frequent behavior pattern producing approach by incrementally propagating and aggregating the local frequent behavior patterns. Based on the local frequent patterns and the global frequent patterns, we depicted the malicious node detection process and the examples of using our model. The simulation results indicated that our model could effectively detect malicious behavior, such as collusion, Sybil and file polluter. In our future work, we will focus our efforts on both the settings of keys used to perform frequent behavior pattern mining and the application of our model in other types of P2P networks.