Traffic Anomaly Detection Model Using K-Means and Active Learning Method-Reference-Cited by-同舟云学术

Traffic Anomaly Detection Model Using K-Means and Active Learning Method

Published:2022-03-23 Issue:5 Volume:24 Page:2264-2282
ISSN:1562-2479
Container-title:International Journal of Fuzzy Systems
language:en
Short-container-title:Int. J. Fuzzy Syst.

Author:

Liao Niandong,Li Xiaoxuan

Funder

“Practical Innovation and Enhancement of Entrepreneurial Ability” for Professional Degree Postgraduates of Changsha University of Science & Technology

hunan provincial engineering research center of electric transportation and smart distribution network

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s40815-022-01269-0.pdf

Reference45 articles.

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3. Arunraj, N., Hable, R., et al.: Comparison of supervised, semi-supervised and unsupervised learning methods in network intrusion detection system (NIDS) application, Anwendungen und Konzepte der. Wirtschaftsinformatik 6, 10–19 (2017)

4. Al-Yaseen, W.L., Othman, Z.A., Nazri, M.Z.A.: Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system. Expert Syst. Appl. 67, 296–303 (2017)

5. Heller, K., Svore, K. et al.: One class support vector machines for detecting anomalous windows registry accesses.In: Proceedings of Workshop on Data Mining for Computer Security (2003)

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