一种新的自适应网络入侵检测模型

在基于免疫模型的网络入侵检测中，因模型对自体的动态变化缺乏自适应性导致高的误报率和漏报率。为了提高网络入侵检测模型在动态环境下的自适应性，使模型能更好地应对不断变化的外部环境，提出了一种新的自适应网络入侵检测模型。模型中详细阐述了自体的演化，对现有否定选择模型中检测器生成存在问题进行了分析，提出新的检测器生成算法，随着自体的在线自动更正，检测器可以始终保持同步更新。结果表明该模型具有很好自适应性和动态性，可以对入侵行为进行有效的识别。

A New Model of Self-Adaptive Network Intrusion Detection

Since the model lack of self-adaptive to the self dynamic changes in the network intrusion detection model based on the immunity,its lead to high rates of false positives and false negative.In order to improves the self-adaptive of the network intrusion detection model under the dynamic environment and make the model can work well under the changeable environment,a new model of self-adaptive network intrusion detection based on negative selection algorism was presented to tackle the problem of self continuously changeable in network intrusion detection.The evolvement of self are fully expounded;a new method to generate and evolve of detectors is put forward that can update automatically to keep synchronization with self.The result shows that the model has the properties of adaptability,dynamics,and can identify the intrusion effectively.