基于组合智能的网络入侵检测模型

Internet intrusion detection model based on integrated intelligence

提出了一种基于组合智能的入侵检测模型,入侵检测中存在的主要问题是数据特征属性多,以及存在不完整数据问题。如果一个实际的入侵检测系统不对数据进行处理,则无法准确地检测到入侵行为。为解决这个问题,本文利用组合智能方法,通过对数据特征属性的约简,将输入信息模糊化和数据本身的训练和学习,能够解决入侵检测中存在的问题,该模型有较好的数据处理能力,实验结果表明引入组合智能后的入侵检测效率大大提高。

An intrusion detection （ID） model is proposed based on the integrated intelligence. A major problem of ID is that large data characteristic and exists half-baked data. If an actual ID cannot process data, so the intrusion detection behavior cannot be detected by the detection system accrual. To solve the problem ,integrated intelligence technique is utilized to the IDS, though data feature reduction, the import information is blurred, and the data is trained and learned, solving the problems of the IDS. The model has the characters of high data process rate, the efficiency of ID is improved.