This research provides a comparative analysis of the use of Vector Autoregressive models for network anomaly detection and prediction. It starts by giving a brief overview of the models and going over the two versions that are available for network anomaly detection. Ultimately, the study offers an empirical assessment of the two types of models, just considering how well they detect and forecast anomalies overall. The results show that the unmarried-node anomaly detection performance of the model is superior. Simultaneously, the Adaptive Learning version is particularly effective in identifying anomalies among a few nodes. The fundamental reasons for the differences in the two fashions' overall performance are also examined in this research. This work provides a comparative analysis of two widely utilized algorithmic approaches: vector autoregressive models and community anomaly detection and prediction. Each method's effectiveness is assessed using two different network datasets: one based on real-world global measurements of latency and mobility ranges, and the other focused on a fictional community. The study also examines the trade-offs between employing the versus other modern and classic techniques, Markov Chain Monte Carlo, and Artificial Neural Networks for network anomaly detection. Finally, it provides an overview of the advantages and disadvantages of each technique as well as suggestions for improving performance.
Z. Xu, Z. Cheng, and B. Guo, “A hybrid data-driven framework for satellite telemetry data anomaly detection,” Acta Astronautica, vol. 205, pp. 281–294, Apr. 2023, doi: 10.1016/j.actaastro.2023.02.009.
S. Saha, A. Haque, and G. Sidebottom, “Analyzing the Impact of Outlier Data Points on Multi-Step Internet Traffic Prediction Using Deep Sequence Models,” IEEE Transactions on Network and Service Management, vol. 20, no. 2, pp. 1345–1362, Jun. 2023, doi: 10.1109/tnsm.2023.3262406.
D. Dwivedi, P. K. Yemula, and M. Pal, “DynamoPMU: A Physics Informed Anomaly Detection, Clustering, and Prediction Method Using Nonlinear Dynamics on μ PMU Measurements,” IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–9, 2023, doi: 10.1109/tim.2023.3327481.
Y. Wang, Z. Yu, and L. Zhu, “Intrusion detection for high-speed railways based on unsupervised anomaly detection models,” Applied Intelligence, vol. 53, no. 7, pp. 8453–8466, Jul. 2022, doi: 10.1007/s10489-022-03911-8.
D. Borda, M. Bergagio, M. Amerio, M. C. Masoero, R. Borchiellini, and D. Papurello, “Development of Anomaly Detectors for HVAC Systems Using Machine Learning,” Processes, vol. 11, no. 2, p. 535, Feb. 2023, doi: 10.3390/pr11020535.
M. Alizadeh and J. Ma, “High-dimensional time series analysis and anomaly detection: A case study of vehicle behavior modeling and unhealthy state detection,” Advanced Engineering Informatics, vol. 57, p. 102041, Aug. 2023, doi: 10.1016/j.aei.2023.102041.
J. Yang, Z. Yue, and Y. Yuan, “Deep probabilistic graphical modeling for robust multivariate time series anomaly detection with missing data,” Reliability Engineering & System Safety, vol. 238, p. 109410, Oct. 2023, doi: 10.1016/j.ress.2023.109410.
Y.-X. Lu, X.-B. Jin, D.-J. Liu, X.-C. Zhang, and G.-G. Geng, “Anomaly Detection Using Multiscale C-LSTM for Univariate Time-Series,” Security and Communication Networks, vol. 2023, pp. 1–12, Jan. 2023, doi: 10.1155/2023/6597623.
M. Abdallah et al., “Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets,” Sensors, vol. 23, no. 1, p. 486, Jan. 2023, doi: 10.3390/s23010486.
Q. He, G. Wang, H. Wang, and L. Chen, “Multivariate time-series anomaly detection via temporal convolutional and graph attention networks,” Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5953–5962, Apr. 2023, doi: 10.3233/jifs-222554.
J. Bae, J. H. Lee and S. Kim, “PNI : Industrial Anomaly Detection using Position and Neighborhood Information,” In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, doi: 10.48550/arXiv.2211.12634.
D. Pan and S. Hamdar, “From Traffic Analysis to Real-Time Management: A Hazard-Based Modeling for Incident Durations Extracted Through Traffic Detector Data Anomaly Detection,” Transportation Research Record: Journal of the Transportation Research Board, p. 036119812311744, Jun. 2023, doi: 10.1177/03611981231174445.
A. Copiaco et al., “An innovative deep anomaly detection of building energy consumption using energy time-series images,” Engineering Applications of Artificial Intelligence, vol. 119, p. 105775, Mar. 2023, doi: 10.1016/j.engappai.2022.105775.
H. Liu and L. Li, “Anomaly Detection of High-Frequency Sensing Data in Transportation Infrastructure Monitoring System Based on Fine-Tuned Model,” IEEE Sensors Journal, vol. 23, no. 8, pp. 8630–8638, Apr. 2023, doi: 10.1109/jsen.2023.3254506.
Y. Qiao, J. Lü, T. Wang, K. Liu, B. Zhang, and H. Snoussi, “A Multi-head Attention Self-supervised Representation Model for Industrial Sensors Anomaly Detection,” IEEE Transactions on Industrial Informatics, pp. 1–10, 2023, doi: 10.1109/tii.2023.3280337.
Q. Wang and Q. Shen, “Multivariate time-series anomaly detection,” International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023), Jun. 2023, doi: 10.1117/12.2679609.
M. Jin, H. Y. Koh, Q. Wen, D. Zambon, C. Alippi, G. I. Webb, I. King and S. Pan, “A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection,” 2020, arXiv preprint arXiv:2307.03759.
G. Wang et al., “Anomaly Detection for Data from Unmanned Systems via Improved Graph Neural Networks with Attention Mechanism,” Drones, vol. 7, no. 5, p. 326, May 2023, doi: 10.3390/drones7050326.
C. Ding, J. Zhao, and S. Sun, “Concept Drift Adaptation for Time Series Anomaly Detection via Transformer,” Neural Processing Letters, vol. 55, no. 3, pp. 2081–2101, Aug. 2022, doi: 10.1007/s11063-022-11015-0.
Acknowledgements
This study was supported by the University Innovation Support Project through Sanmyung University in 2023.
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Jongseong Choi
Jongseong Choi
Department of Mechanical Engineering, The State University of New York (SUNY Korea), Incheon Free Economic Zone Authority, South Korea.
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Cite this article
Ok-Hue Cho and Jongseong Choi, “A Comparative Analysis of IoT based Network Anomaly Detection and Prediction Using Vector Autoregressive Models”, Journal of Machine and Computing, pp. 127-137, January 2024. doi: 10.53759/7669/jmc202404013.
