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Trust-aware FuzzyClus-Fuzzy NB: intrusion detection scheme based on fuzzy clustering and Bayesian rule

机译:信任感知的FuzzyClus-Fuzzy NB:基于模糊聚类和贝叶斯规则的入侵检测方案

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摘要

The dynamic nature of the nodes on the mobile ad hoc network (MANET) imposes security issues in the network and most of the Intrusion detection methods concentrated on the energy dissipation and obtained better results, whereas the trust remained a hectic factor. This paper proposes a trust-aware scheme to detect the intrusion in the MANET. The proposed Trust-aware fuzzy clustering and fuzzy Naive Bayes (trust-aware FuzzyClus-Fuzzy NB) method of detecting the intrusion is found to be effective. The fuzzy clustering concept determines the cluster-head to form the clusters. The proposed BDE-based trust factors along with the direct trust, indirect trust, and the recent trust, hold the information of the nodes and the fuzzy Naive Bayes determine the intrusion in the nodes using the node trust table. The simulation results convey the effectiveness of the proposed method and the proposed method is analyzed based on the metrics, such as delay, energy, detection rate, and throughput. The delay is in minimum at a rate of 0.00434, with low energy dissipation of 9.933, high detection rate of 0.623, and greater throughput of 0.642.
机译:移动自组织网络(MANET)上节点的动态性质强加了网络中的安全性问题,并且大多数入侵检测方法都集中在能耗上并获得了更好的结果,而信任仍然是一个忙碌的因素。本文提出了一种信任感知方案来检测MANET中的入侵。发现提出的信任感知模糊聚类和模糊朴素贝叶斯(信任感知FuzzyClus-Fuzzy NB)检测方法是有效的。模糊聚类概念确定聚类头以形成聚类。所提出的基于BDE的信任因子以及直接信任,间接信任和最近信任,都保存了节点的信息,并且模糊朴素贝叶斯算法使用节点信任表来确定对节点的入侵。仿真结果表明了该方法的有效性,并基于时延,能量,检测率和吞吐量等指标对方法进行了分析。延迟的最小速率为0.00434,低能耗为9.933,高检测率为0.623,且吞吐量为0.642。

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