Diagnosis on Computational Grids for Detecting Intelligent Cheating Nodes.
Applications on reputation-based computational grids are prone to attacks from malicious nodes. These nodes may not only corrupt the result from processed jobs, but also intentionally acquire a good reputation so as to obtain privileges to damage other nodes. In order to detect and isolate intelligent cheating nodes from a P2P grid computing, this work proposes a system-level diagnosis model, using a strategy based on voting and honeypots. The model is evaluated by means of scenarios that take into account different percentages of malicious and cheating nodes. Achieved results show the model’s robustness and efficiency, once all cheating nodes can be detected, with an accuracy of 99.4% of jobs being correctly processed.