面向蓄意攻击的网络异常检测方法
Journal of Northeastern University(Natural Science)(2020)
Abstract
针对复杂网络受蓄意攻击频繁,而现有的检测方法大多忽略全局拓扑突变特征的问题.从网络全局拓扑的异常演化特征出发,提出网络路径相对变化系数(network path change coefficient,NPCC)r,量化节点间传输路径的变化.由斐波那契数列衍生出斐波那契演化域,用于区分正常和异常演化.将r作为核心度量参量,构建斐波那契演化域,形成网络异常检测方法,实现对异常的判定.结果表明,该检测方法的平均准确率为90% 以上,高于最大公共子图(maximum common subgraph,MCS)及图编辑距离(graph edit distance,GED)的准确率,证明了所提检测方法的有效性.
MoreKey words
intentional attack,anomaly,detection,network
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