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Intrusion Detection Based on An Improved ART2 Neural Network

机译:基于改进的ART2神经网络的入侵检测

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An Intrusion detection algorithm based on an improved ART-2 Neural Networks is proposed in this paper. Based on traditional ART-2 neural networks, a prepositive matching system and an amplitude analysis procedure are employed. The prepositive matching system is employed to hasten the pattern matching and provide stable clustering while training the ANN. It also overcomes the limitation of sensibility to noise existing in ART2. The simulation results showed that the algorithm is efficient and precise. The information of the stable clustering can be used to provide supports for decision-making of defining normal and abnormal behavior patterns.
机译:提出了一种基于改进的ART-2神经网络的入侵检测算法。在传统的ART-2神经网络的基础上,采用了正匹配系统和幅度分析程序。在训练人工神经网络的同时,采用正匹配系统来加速模式匹配并提供稳定的聚类。它还克服了ART2中存在的噪声敏感性限制。仿真结果表明,该算法是有效且精确的。稳定聚类的信息可用于为定义正常和异常行为模式的决策提供支持。

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