Presentation 2014-07-10
Human Error Tolerant Anomaly Detection through Time-Periodic Packet Sampling
Masato UCHIDA,
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Abstract(in English) This paper focuses on an anomaly detection method that uses a baseline model describing the normal behavior of network traffic as the basis for comparison with the audit network traffic. In the anomaly detection method, an alarm is raised if a pattern in the current network traffic deviates from the baseline model. The baseline model is often trained using normal traffic data extracted from traffic data for which all instances (i.e., packets) are manually labeled by human experts in advance as either normal or anomalous. However, since humans are fallible, some errors are inevitable in labeling traffic data. Therefore, in this paper, we propose an anomaly detection method that is tolerant to human errors in labeling traffic data. The fundamental idea behind the proposed method is to take advantage of the lossy nature of packet sampling for the purpose of correcting/preventing human errors in labeling traffic data. By using real traffic traces, we show that the proposed method can better detect anomalies regarding TCP SYN packets than the method that relies only on human labeling.
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Keyword(in English) anomaly detection / human error / packet sampling
Paper # CQ2014-16
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Committee CQ
Conference Date 2014/7/3(1days)
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Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Human Error Tolerant Anomaly Detection through Time-Periodic Packet Sampling
Sub Title (in English)
Keyword(1) anomaly detection
Keyword(2) human error
Keyword(3) packet sampling
1st Author's Name Masato UCHIDA
1st Author's Affiliation Department of Electrical, Electronics and Computer Engineering, Chiba Institute of Technology()
Date 2014-07-10
Paper # CQ2014-16
Volume (vol) vol.114
Number (no) 131
Page pp.pp.-
#Pages 6
Date of Issue