1. 解放军理工大学指挥自动化学院,江苏,南京,210007
2. 解放军防空兵指挥学院,河南,郑州,450052
3. 解放军理工大学指挥自动化学院江苏南京,210007
4. 解放军防空兵指挥学院河南郑州,450052
纸质出版:2011
移动端阅览
钱叶魁, 陈鸣. 面向PCA异常检测器的毒害攻击和防御机制[J]. 电子学报, 2011,39(3):543-548.
QIAN Ye-kui, CHEN Ming. Poison Attack and Defense Strategies on PCA-Based Anomaly Detector[J]. Acta Electronica Sinica, 2011, 39(3): 543-548.
网络流量异常检测对于保证网络稳定高效运行极为重要.目前基于主成分分析(PCA)的全网络异常检测算法虽然发挥了关键作用
但它还存在着受毒害攻击而失效的问题.为此
深入分析了毒害攻击的机制并对其进行了分类
提出了量化毒害流量的两个测度
并给出了3种新的毒害攻击机制;提出了一种基于健壮PCA的异常检测算法RPCA以抵御毒害攻击.模拟试验结果表明
RPCA算法在受到多种毒害攻击时仍然具有很好的检测性能
明显优于PCA异常检测器
且运行时间能够满足实际网络异常检测的需求.
Network traffic anomaly detection is crucial to guarantee stable and effective network operation.Nowadays
although PCA-based network-wide anomaly detector plays an important role
it cannot detect anomalous network traffic effectively in face of poison attacks.In order to solve poison attack problem aiming at PCA-based anomaly detector
poison attack strategies are investigated and classified
two metrics for quantifying poison traffic are proposed and three novel poison attack strategies are put forward.A robust PCA-based anomaly detection algorithm (for short RPCA) is proposed to resist poison attacks.Simulation experiment results show that RPCA algorithm can still perform very well in face of poison attacks
obviously superior to PCA-based anomaly detector
and its running time can satisfy the need of practical network anomaly detection.
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