Article Sidebar

PDF
Date Published: 30/11/2022
Online Published: 30/11/2022
Abstract Views: 115
Views PDF: 57
DOI: https://doi.org/10.54607/hcmue.js.19.11.3625(2022)
Issue
Vol. 19 No. 11 (2022)
Section
Articles
How to Cite
Nguyễn , Q. H. (2022). ANOMALY DETECTION OF ROUTER DEVICES BY CLASSIFICATION TECHNIQUES. HCMUE Journal of Science, 19(11), 1878. https://doi.org/10.54607/hcmue.js.19.11.3625(2022)

ANOMALY DETECTION OF ROUTER DEVICES BY CLASSIFICATION TECHNIQUES

Quốc Huy Nguyễn

Main Article Content

Abstract

Detecting early the anomaly signal of routers helps to predict errors and to prepare suitable solutions. Anomaly signals are analysed from the data log of devices. In this study, we have proposed an approach to detect anomaly signals from log files of routers using classification techniques. The log files BGL from the Usenix organization are collected and labelled based on the experience of many experts. Feature extraction is performed before training and testing the model. The results are efficient in almost realistic environments and especially confirm the assumption of the important features via our observation process.

 

Keywords

anomaly detection, classification techniques, feature extraction, log file classification, router devices

Article Details

References

Ertam, F., & Kaya, M. (2018). Classification of Firewall Log Files with Multiclass Support Vector Machine. In: A, Varol, M. Karabatak and C. Varol (editors). International Symposium on Digital Forensic and Security, 22-25, Antalya, Turkey. IEEE. Piscataway, New Jersey, 1-4.
Guo, A., & Yang, T. (2016). Research and improvement of feature words weight based on TFIDF algorithm. In: Information Technology, Networking, Electronic and Automation Control Conference, 20-22 May 2016, Chongqing, China. IEEE. 415-419.
He, P., Zhu, J., Zheng, Z.,& Lyu, M. R (2017). Drain: An Online Log Parsing Approach with Fixed Depth Tree. In Proceedings of the IEEE International Conference on Web Services (ICWS), Honolulu, HI, USA, 25-30 June 2017.
IBM. Drain3. Retrieved from https://github.com/IBM/Drain3 (accessed on 10 January 2022)
Sokolova, M., & Lapalm, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45, 427-437.
Usenix. The HPC4 Data. Retrieved from https://www.usenix.org/cfdr-data#hpc4 (accessed on 20 February 2022).
Yadav, R. B., Kumar, P. S., & Dhavale, S. V. (2020). A Survey on Log Anomaly Detection using Deep Learning. In Proceedings of the 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). Noida, India, 4-5 June 2020, 1215-1220.
Ying, S., Wang, B., Wang, L., Li, Q., Zhao, Y., Shang, J., … Geng, J. (2021). An Improved KNN-Based Efficient Log Anomaly Detection Method with Automatically Labeled Samples. ACM Trans. Knowl. Discov, 15(3), 1-22.
Zhao, X., Wang, H. Xiao, & Chi, X. (2018). Improvement of the Log Pattern Extracting Algorithm Using Text Similarity. In: International Parallel and Distributed Processing Symposium Workshops, 21-25, May 2018, Vancouver, BC, Canada. IEEE. Los Alamitos, California, 507-514.