基于高速精确遗传算法神经网络的网络入侵检测方法

Jingwen Tian, Meijuan Gao
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引用次数: 21

摘要

针对网络入侵行为具有不确定性、复杂性、多样性和动态倾向性等特点,结合神经网络的优势,提出了一种基于高速、精确的遗传算法神经网络的入侵检测方法。高速精密遗传算法神经网络将自适应浮点码遗传算法与BP网络相结合,具有更高的精度和更快的收敛速度。构造了网络结构,给出了算法流程。对入侵行为的影响因素进行了讨论和分析。网络入侵检测方法利用高速精确遗传算法神经网络强大的自学习能力和更快的收敛速度,通过学习典型的入侵特征信息,能够快速有效地检测出各种入侵行为。实验结果表明,该入侵检测方法是可行和有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Network Intrusion Detection Method Based on High Speed and Precise Genetic Algorithm Neural Network
Aimed at the network intrusion behaviors are characterized with uncertainty, complexity, diversity and dynamic tendency and the advantages of neural network, an intrusion detection method based on high speed and precise genetic algorithm neural network is presented in this paper. The high speed and precise genetic algorithm neural network is combined the adaptive and floating-point code genetic algorithm with BP network which has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong self-learning and faster convergence of high speed and precise genetic algorithm neural network, the network intrusion detection method can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. The experimental result shows that this intrusion detection method is feasible and effective.
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