CAJ | 학술논문

根据异常流量对网络自相似的影响,通过研究在流量正常和异常情况下表征自相似程度的Hurst参数分布特点的不同,设计一种异常流量动态自适应检测方法.该方法采用小波分析估计Hurst参数,根据网络自相似程度自适应地调整检测阈值.对MIT林肯实验室的入侵检测数据测试结果表明,该检测方法具有较好的动态自适应性、较高的检测率及较快的检测速度.
근거이상류량대망락자상사적영향,통과연구재류량정상화이상정황하표정자상사정도적Hurst삼수분포특점적불동,설계일충이상류량동태자괄응검측방법.해방법채용소파분석고계Hurst삼수,근거망락자상사정도자괄응지조정검측역치.대MIT림긍실험실적입침검측수거측시결과표명,해검측방법구유교호적동태자괄응성、교고적검측솔급교쾌적검측속도.
According to the impact of abnormal traffic on network self-similarity,an abnormal traffic detection method is designed through comparing the difference of Hurst parameter distribution under network normal and abnormal traffic conditions.This method uses wavelet analysis to calculate the Hurst parameter and the detection threshold can be self-adjusted according to the extent of network self-similarity.Test results on data sets of Lincoln Lab of MIT dernonstrate that the new detection method has the characteristic of dynamic self-adaptive,higher detection rate and faster detection speed.