Volume 33 Issue 3
May  2024
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Article Contents
Zequn NIU, Jingfeng XUE, Yong WANG, et al., “QARF: A Novel Malicious Traffic Detection Approach via Online Active Learning for Evolving Traffic Streams,” Chinese Journal of Electronics, vol. 33, no. 3, pp. 645–656, 2024 doi: 10.23919/cje.2022.00.360
Citation: Zequn NIU, Jingfeng XUE, Yong WANG, et al., “QARF: A Novel Malicious Traffic Detection Approach via Online Active Learning for Evolving Traffic Streams,” Chinese Journal of Electronics, vol. 33, no. 3, pp. 645–656, 2024 doi: 10.23919/cje.2022.00.360

QARF: A Novel Malicious Traffic Detection Approach via Online Active Learning for Evolving Traffic Streams

doi: 10.23919/cje.2022.00.360
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  • Author Bio:

    Zequn NIU was born in 1994. He received the B.E. degree in software engineering from Beijing Institute of Technology, Beijing, China. He is a Ph.D. candidate of Beijing Institute of Technology. His research interests include data mining and traffic analysis. (Email: niuzq@ouchn.edu.cn)

    Jingfeng XUE was born in 1975. He is a Professor and Ph.D. Supervisor in Beijing Institute of Technology. His main research interests focus on network security, data security and software security

    Yong WANG was born in 1975. She is an Associate Professor of Beijing Institute of Technology. Her main research interests focus on cyber security and machine learning. (Email: wangyong@bit.edu.cn)

    Tianwei LEI was born in 1993. She received the M.E. degree in software engineering from Beijing Institute of Technology. She is a Ph.D. candidate of Beijing Institute of Technology. Her research interests include software fault and malware analysis

    Weijie HAN was born in 1980. He received the Ph.D. degree from Beijing Institute of Technology. He is currently a Lecture in Space Engineering University. His research interests include malware detection and APT detection

    Xianwei GAO was born in 1978. He received the Ph.D. degree from Beijing Institute of Technology. He has many years of experience in security operation and software engineering in a famous IT enterprise. His research interests mainly focus on artificial intelligence and cyber security

  • Corresponding author: Email: wangyong@bit.edu.cn
  • Received Date: 2022-10-24
  • Accepted Date: 2023-02-14
  • Available Online: 2023-07-14
  • Publish Date: 2024-05-05
  • In practical abnormal traffic detection scenarios, traffic often appears as drift, imbalanced and rare labeled streams, and how to effectively identify malicious traffic in such complex situations has become a challenge for malicious traffic detection. Researchers have extensive studies on malicious traffic detection with single challenge, but the detection of complex traffic has not been widely noticed. Queried adaptive random forests (QARF) is proposed to detect traffic streams with concept drift, imbalance and lack of labeled instances. QARF is an online active learning based approach which combines adaptive random forests method and adaptive margin sampling strategy. QARF achieves querying a small number of instances from unlabeled traffic streams to obtain effective training. We conduct experiments using the NSL-KDD dataset to evaluate the performance of QARF. QARF is compared with other state-of-the-art methods. The experimental results show that QARF obtains 98.20% accuracy on the NSL-KDD dataset. QARF performs better than other state-of-the-art methods in comparisons.
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