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Intrusion detection system based on improved BP Neural Network and Decision Tree

机译:基于改进的BP神经网络和决策树的入侵检测系统

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摘要

According to the attributes of both BP Neural Network and Decision Tree, this paper presents an advanced complex-algorithm model in order to improve the ability of intrusion detection. The simulating results show that the new system not only can increase the average detection rate and reduce the failing, but it can also be more effective to simplify the complexity, raise the detection speed and promote the accuracy by use of abstracting rules and paralleled dealing method in matching process.
机译:根据BP神经网络和决策树的属性,提出了一种先进的复杂算法模型,以提高入侵检测的能力。仿真结果表明,新系统不仅可以提高平均检测率,减少故障,而且可以通过抽象规则和并行处理方法,简化复杂度,提高检测速度,提高准确率,更加有效。在匹配过程中。

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