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Open Access

Efficient Feature Extraction Using Apache Spark for Network Behavior Anomaly Detection

Xiaoming Ye, Xingshu Chen( ), Dunhu Liu, Wenxian Wang, Li Yang, Gang Liang, Guolin Shao
School of Cybersecurity, Chengdu University of Information Technology, Chengdu 610225
College of Cybersecurity, Sichuan University, Chengdu 610065, China.
School of Management, Chengdu University of Information Technology, Chengdu 610103, China.
College of Compute Science, Sichuan University, Chengdu 610065, China.
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Abstract

Extracting and analyzing network traffic feature is fundamental in the design and implementation of network behavior anomaly detection methods. The traditional network traffic feature method focuses on the statistical features of traffic volume. However, this approach is not sufficient to reflect the communication pattern features. A different approach is required to detect anomalous behaviors that do not exhibit traffic volume changes, such as low-intensity anomalous behaviors caused by Denial of Service/Distributed Denial of Service (DoS/DDoS) attacks, Internet worms and scanning, and BotNets. We propose an efficient traffic feature extraction architecture based on our proposed approach, which combines the benefit of traffic volume features and network communication pattern features. This method can detect low-intensity anomalous network behaviors and conventional traffic volume anomalies. We implemented our approach on Spark Streaming and validated our feature set using labelled real-world dataset collected from the Sichuan University campus network. Our results demonstrate that the traffic feature extraction approach is efficient in detecting both traffic variations and communication structure changes. Based on our evaluation of the MIT-DRAPA dataset, the same detection approach utilizes traffic volume features with detection precision of 82.3% and communication pattern features with detection precision of 89.9%. Our proposed feature set improves precision by 94%.

Keywords

feature extractiongraph theorynetwork behavioranomaly detectionApache Spark

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Tsinghua Science and Technology
Volume 23 Issue 5,
October 2018
Pages 561-573
DOI: 10.26599/TST.2018.9010021
Cite this article:
Ye X, Chen X, Liu D, et al. Efficient Feature Extraction Using Apache Spark for Network Behavior Anomaly Detection. Tsinghua Science and Technology, 2018, 23(5): 561-573. https://doi.org/10.26599/TST.2018.9010021

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Received: 24 September 2017
Accepted: 29 September 2017
Published: 17 September 2018
© The author(s) 2018
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