Design of a linear regression model-based Internet exit anomaly detection method

[1] Xie, K., Li, X., Wang, X., et al. (2017). Fast tensor factorization for accurate internet anomaly detection. IEEE/ACM Transactions on Networking, 25(6), 3794-3807. Search in Google Scholar

[2] Forestiero, A. (2021). Metaheuristic algorithm for anomaly detection in Internet of Things leveraging on a neural-driven multiagent system. Knowledge-Based Systems, 228, 107241. Search in Google Scholar

[3] Wang, J., Tang, Y., He, S., et al. (2020). LogEvent2vec: LogEvent-to-vector based anomaly detection for large-scale logs in Internet of Things. Sensors, 20(9), 2451. Search in Google Scholar

[4] NG, B. A., & Selvakumar, S. (2020). Anomaly detection framework for Internet of things traffic using vector convolutional deep learning approach in fog environment. Future Generation Computer Systems, 113, 255-265. Search in Google Scholar

[5] Reddy, D. K. K., Behera, H. S., Nayak, J., et al. (2021). Deep neural network-based anomaly detection in Internet of Things network traffic tracking for the applications of future smart cities. Transactions on Emerging Telecommunications Technologies, 32(7), e4121. Search in Google Scholar

[6] Aljawarneh, S. A., & Vangipuram, R. (2020). GARUDA: Gaussian dissimilarity measure for feature representation and anomaly detection in Internet of things. The Journal of Supercomputing, 76(6), 4376-4413. Search in Google Scholar

[7] Genge, B., Haller, P., & Enăchescu, C. (2019). Anomaly detection in aging industrial Internet of Things. IEEE Access, 7, 74217-74230. Search in Google Scholar

[8] Yanagisawa, D., & Yamazaki, K. (2021). Detecting aggressive agents in egress process by using conflict data in cellular automaton model. Journal of Intelligent Transportation Systems, (4), 1-18. Search in Google Scholar

[9] Ullah, I., & Mahmoud, Q. H. (2021). Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks. IEEE Access, PP(99), 1-1. Search in Google Scholar

[10] Leman Akoglu, Hanghang Tong, Danai Koutra. (2015). Graph-based anomaly detection and description: a survey. Data Mining and Knowledge Discovery, 29, 626-688. Search in Google Scholar

[11] Mothukuri, V., Khare, P., Parizi, R. M., et al. (2021). Federated-Learning-Based Anomaly Detection for IoT Security Attacks. IEEE Internet of Things Journal, 9(4), 2545-2554. Search in Google Scholar

[12] Cook, A. A., Mısırlı, G., Fan, Z. (2019). Anomaly detection for IoT time-series data: A survey. IEEE Internet of Things Journal, 7(7), 6481-6494. Search in Google Scholar

[13] Bahaa Al-Musawi, Philip Branch, Grenville Armiage. (2016). BGP Anomaly Detection Techniques: A Survey. IEEE Communications Surveys & Tutorials, 2622, 2240. Search in Google Scholar

[14] Tama, B. A., Nkenyereye, L., Islam, S. M. R., et al. (2020). An enhanced anomaly detection in web traffic using a stack of classifier ensemble. IEEE Access, 8, 24120-24134. Search in Google Scholar

[15] Xie, K., Li, X., Wang, X., et al. (2018). On-line anomaly detection with high accuracy. IEEE/ACM Transactions on Networking, 26(3), 1222-1235. Search in Google Scholar

[16] Emmendorfer, L. R., & Dimuro, G. P. (2021). A point interpolation algorithm resulting from weighted linear regression. Journal of Computational Science, 50(3). Search in Google Scholar

[17] Nawaratne, R., Alahakoon, D., De Silva, D., et al. (2019). Spatiotemporal anomaly detection using deep learning for real-time video surveillance. IEEE Transactions on Industrial Informatics, 16(1), 393-402. Search in Google Scholar

[18] Ahmed, A. A. M., Sharma, E., Jui, S. J. J., Deo, R. C., Nguyen-Huy, T., & Ali, M. (2022). Kernel ridge regression hybrid method for wheat yield prediction with sate llite-derived predictors. Re mote Sensing, 14. Search in Google Scholar