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Web服务和网络应用已经成为互联网上的最重要的沟通渠道之一,而相当一大部份的计算机网络安全漏洞都是由于Web自身的易受攻击性而导致的,且网络入侵又越来越难以被检测到。本文介绍了一种基于免疫系统和模糊逻辑的自适应网格入侵检测模型。通过改进候选项目集的产生方式,该模型分别建立了自然行为模式与入侵行为模式的模糊规则集。比较这两种规则集的不同,进而检测到网络入侵。另外,模型自动更新了基于免疫的模糊规则,从而可以提高检测新的网络入侵的能力。实验证明,在辨认不正常的入侵行为中,与没有进行模型更新和采用模糊规则的模型相比,该模型具有更高的效率。
Web services and web applications have become one of the most important communication channels on the Internet. A considerable part of the computer network security loopholes are caused by the vulnerability of the web itself, and the network invasion is getting more and more Hard to be detected. This paper introduces an adaptive grid intrusion detection model based on immune system and fuzzy logic. By improving the generation of candidate itemsets, this model establishes fuzzy rule sets of natural behavior patterns and intrusion patterns, respectively. Compare the difference between the two rule sets, and then detect the network invasion. In addition, the model automatically updates immune-based fuzzy rules to improve the ability to detect new network intrusion. Experiments show that the model is more efficient than the model without model updating and fuzzy rules in identifying abnormal intrusion.