基于神经网络GCBP算法的网络入侵检测方法

Yan Li, Wang Jie
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引用次数: 3

摘要

本文提出了一种基于神经网络GCBP算法的网络入侵检测方法。通过对BP算法与GCBP算法的分析比较,发现GCBP算法克服了传统BP算法收敛速度慢、容易陷入局部极小值的缺点。该方法的应用效果更好,精度更高,自适应能力更强。我也表现出了更快的学习能力。在很大程度上可以实现对未知数据包的检测,为高效、准确地实现入侵检测提供了一种有效的方法。
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The method of network intrusion detection based on the neural network GCBP algorithm
In this paper, it presented the method of network intrusion detection based on the neural network GCBP algorithm. By analyzing and comparing the BP algorithm with the GCBP algorithm, what learned is that the GCBP algorithm had overcome the weaknesses, which traditional BP algorithm has of slow convergent speed and easily getting into local minimum. Applicable effects of the method of the intrusion detection are much better, more highly accurate, and greater adaptively. I also showed the ability of more quickly learning. To a great extent, it can achieve the detection of unknown data packets, providing an effective way to efficiently and accurately realize intrusion detection.
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