Abstract: The immune model with low detection efficiency can not adapt to the current network intrusion detection. This paper presents an improved immune model based on double protecting system, combing the biological immune theory with the fuzzy mathematics principle, with two detection processes. According to the Fuzzy Pattern Recognition (F-PR), some useful data samples are condensed, and a rule is abstracted from the special data sample as important evidence for detection. Experimental results show that this model can achieve low false alarm rate and missing rate.

Key words: intrusion detection, artificial immune, Fuzzy Pattern Recognition(F-PR)

摘要： 低效率检测能力的免疫模型不能满足当今网络入侵检测的需要，该文提出一个双层防护体系结构，将免疫原理和模糊数学理论相结合，具有2次检测过程。该模型利用了模糊模式识别理论对数据样本进行数据浓缩，从给定学习样本数据中提取一组规则作为检测的重要依据。实验表明，该模型可以有效降低漏报率及误报率，具有较大的参考价值，在实际中有良好的应用前景。

关键词: 入侵检测, 人工免疫, 模糊模式识别

CLC Number: 

• TP393.08