A SURVEY ON DETECT - DISCOVERING AND EVALUATING TRUST USING EFFICIENT CLUSTERING TECHNIQUE FOR MANET S

K. Sudharson, N. Partheeban
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Abstract

Analyzing and predicting behavior of node can lead to more secure and more appropriate defense mechanism for attackers in the Mobile Adhoc Network. In this work, models for dynamic recommendation based on fuzzy clustering techniques, applicable to nodes that are currently participate in the transmission of Adhoc Network. The approach focuses on both aspects of MANET mining and behavioral mining. Applying fuzzy clustering and mining techniques, the model infers the node's preferences from transmission logs. The fuzzy clustering approach, in this study, provides the possibility of capturing the uncertainty among node's behaviors. The results shown are promising and proved that integrating fuzzy approach provide us with more interesting and useful patterns which consequently making the recommender system more functional and robust.
基于高效聚类技术的客户端信任检测、发现与评估研究综述
分析和预测节点的行为可以为移动自组网中的攻击者提供更安全、更合适的防御机制。本文研究了基于模糊聚类技术的动态推荐模型,该模型适用于当前参与Adhoc网络传输的节点。该方法侧重于MANET挖掘和行为挖掘两个方面。该模型采用模糊聚类和挖掘技术,从传输日志中推断节点的偏好。在本研究中,模糊聚类方法提供了捕获节点行为之间不确定性的可能性。结果表明,集成模糊方法为我们提供了更多有趣和有用的模式,从而使推荐系统更具功能性和鲁棒性。
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