结合危险理论的故障检测免疫模型

An Immune Model for Network Fault Detection Based on Danger Theory

摘要:

为了实现快速准确的网络故障检测与诊断,将危险理论与动态克隆选择算法相结合,提出了用于网络故障检测的危险理论免疫模型;并针对网络故障特点,对危险理论与动态克隆选择算法进行了改进;首先采用危险理论模型对抗原进行危险信号浓度识别,并利用成熟检测器检测已知故障类型,其次用改进的动态克隆选择算法对未知故障进行有效的学习;通过对多种网络故障类型检测的仿真实验,证明了新模型不仅具有更好的检测效果和动态适应性,而且能够提高检测效率与准确率。

Abstract:

Aiming at the fast and accurate network fault detection and diagnosis, combining the danger theory with dynamic clonal selection algorithm, a danger theory based immune model used for network fault detection is proposed. And according to the characteristics of network failure to improve danger theory and dynamic clonal selection algorithm.Firstly, the concentration of antigen danger signal is recognized by using the danger theory model,and using the mature detector to detect known fault type. Then an improved clonal selection algorithm is set up to learn the unknown fault types. Experiments were undertaken with various type of network fault diagnosis to demonstrate that the model not only has better detection rate and adaptability but also can improve the efficiency and accuracy.