Network anomaly detection by using a time-decay closed frequent pattern

Article

Zhao, Ying, Chen, Junjun, Wu, Di, Teng, Jian, Sharma, Nabin, Sajjanhar, Atul and Blumenstein, Michael. 2019. "Network anomaly detection by using a time-decay closed frequent pattern." Information (Basel). 10 (8). https://doi.org/10.3390/info10080262
Article Title

Network anomaly detection by using a time-decay closed frequent pattern

ERA Journal ID122861
Article CategoryArticle
AuthorsZhao, Ying, Chen, Junjun, Wu, Di, Teng, Jian, Sharma, Nabin, Sajjanhar, Atul and Blumenstein, Michael
Journal TitleInformation (Basel)
Journal Citation10 (8)
Article Number262
Number of Pages18
Year2019
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN2078-2489
Digital Object Identifier (DOI)https://doi.org/10.3390/info10080262
Web Address (URL)https://www.mdpi.com/2078-2489/10/8/262
Abstract

Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user anomaly behavior detection. In real scenarios, the anomaly network behavior may harm the user interests. In this paper, we propose an anomaly detection model based on time-decay closed frequent patterns to address this problem. The model mines closed frequent patterns from the network traffic of each user and uses a time-decay factor to distinguish the weight of current and historical network traffic. Because of the dynamic nature of user network behavior, a detection model update strategy is provided in the anomaly detection framework. Additionally, the closed frequent patterns can provide interpretable explanations for anomalies. Experimental results show that the proposed method can detect user behavior anomaly, and the network anomaly detection performance achieved by the proposed method is similar to the state-of-the-art methods and significantly better than the baseline methods.

Keywordsanomaly detection; frequent pattern; user behavior
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460609. Networking and communications
4602. Artificial intelligence
4604. Cybersecurity and privacy
Byline AffiliationsBeijing University of Chemical Technology, China
University of Technology Sydney
Deakin University
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