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通过对IEEE 802.11 MAC层攻击的分析与分类,提出将节点传帧率和退避时间序列作为行为特征,建立了MAC层攻击检测的人工免疫模型,给出饱和状态下的节点检测算法-基于滑动窗口残差阈值法,并对自私节点的退避时间序列进行编码和基因处理,给出非饱和状态下基于相对位置的子串相关函数匹配的基因检测算法,解决了传统检测算法不能有效检测智能攻击和与现有协议不兼容的局限性.
Based on the analysis and classification of IEEE802.11 MAC layer attacks, this paper proposed the node frame rate and backoff time sequence as the behavioral characteristics, established the artificial immune model of MAC layer attack detection, and presented the algorithm of node detection in saturated state - based on the sliding window Residual threshold method and encoding and gene processing the self-node’s backoff time series. The genetic detection algorithm based on the relative position of the substring correlation function in the unsaturated state is given, which solves the problem that the traditional detection algorithm can not effectively detect the intelligent attack and Limitations incompatible with existing protocols.