基于负载内容的网络异常检测

S. Thorat, A.K. Khandelwal, Bruhadeshwar Bezawada, K. Kishore
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引用次数: 30

摘要

我们提出了基于负载内容的网络异常检测,我们称之为PCNAD。PCNAD是对PAYL系统的改进,PAYL系统被认为是基于有效载荷的完整异常检测系统之一。PAYL考虑了整个有效载荷进行剖面计算和有效的异常检测。有效载荷长度在端口号如21和80上非常高。因此,在高速、高带宽的网络中应用PAYL是很困难的。我们使用CPP(基于内容的有效负载分区)技术,根据有效负载的内容将有效负载划分为不同的分区。PCNAD使用几个CPP分区进行基于负载的异常检测。我们证明了PCNAD在1999年DARPA IDS数据集上的有效性。我们在端口80上观察到97.06%的准确率,仅使用62.64%的数据包有效载荷长度,假阳性率很小。这是对PAYL方法的重大改进,PAYL方法使用100%的数据包有效负载进行异常检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Payload Content based Network Anomaly Detection
We present payload content based network anomaly detection, we call as PCNAD. PCNAD is an improvement to PAYL system which is considered one of the complete systems for payload based anomaly detection. PAYL takes into consideration the entire payload for profile calculation and effectively for anomaly detection. Payload length is very high on port numbers like 21 and 80. Hence it is difficult to apply PAYL on high speed, high bandwidth networks. We use CPP (content based payload partitioning) technique which divides the payload into different partitions depending on content of payload. PCNAD does payload based anomaly detection using a few CPP partitions. We demonstrate usefulness of the PCNAD on the 1999 DARPA IDS data set. We observed 97.06% accuracy on port 80 using only 62.64% packet payload length with small false positive rate. This is a significant improvement over PAYL approach which uses 100% of the packet payload for anomaly detection.
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