基于CMAC网络的异常入侵检测技术

Research on Anomaly Intrusion Detection Based on CMAC

针对入侵检测系统对异常入侵检测的不足,采用CMAC神经网络,将其应用于对异常入侵行为的检测,并给出了基于CMAC的入侵检测模型.由于CMAC网络是一种联想网络,所以它对未知类型的入侵行为有很好的识别能力.同时,它的学习与修正只涉及很少的神经元,所以其速度很快.最后通过试验证明,应用CMAC神经网络的入侵检测系统相对于传统检测技术,在检测率及误判率上都有所提高.

Aiming at the defects of detecting anomaly intrusion for an IDS,CMAC Artificial Neural Network is applied to detect anomaly intrusion behavior of networks,and an IDS model based on CMAC is proposed.Having the capability of association,CMAC can do well in recognizing behaviors of unknown type.In the process of learning and correcting the weight,a few neural cells are changed,so CMAC learns fast.Finally,the result of a simulation experiment testified an increase in both detection rate and misclassified rate of traditional IDS.