INVESTIGATING MACHINE LEARNING ALGORITHMS PERFORMANCE FOR PREDICTING ATTACKS ON SOFTWARE DEFINED NETWORKS
DOI:
https://doi.org/10.24867/21BE25OgundareKeywords:
Big Data, Machine Learning, Software Defined Networks,, Cyber Attacks, Intrusion DetectionAbstract
As technology continues to evolve, the application of Big Data Technology is widening for intrusion and anomaly detection. However, not much has been done within the domain of Software Defined Networks (SDN). This paper explores how Big Data Technology can be used to predict cyber-attacks on SDNs by using historical dataset to build Machine Learning models. Using three widely-known supervised machine learning algorithms-Decision Tree, Random Forest and Naïve Bayes - results show average prediction accuracy of 96.7%. several other techniques were employed to evaluate the performance of the models.
References
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[15] Ullman, S., Poggio, T., Harari, D., Zysman, D., & Seibert, D. (2020). Unsupervised learning - Clustering. Center for Brains, Minds and Machines
[2] Ali, S., Sivaraman, V., Radford, A., & Jha, S. (2015). A survey of securing networks using software defined networking. Reliability IEEE Transactions, 1086-1097
[3] Botezatu, M. (2016). Predicting disk replacement towards reliable data centers. 22nd ACM SIGKDD International Conference on Knowledge Discipline and Data Mining.
[4] Chaves, I. (2018). Hard disk drive failure prediction method based on bayesian network. International Joint Conference on Neural Networks. IEEE
[5] Hu, F., Hao, Q., & Bao, K. (2014). A survey on software deined network and Openflow: From concepts to Implementation. IEEE communications survey tutorials, 2181 - 2206.
[6] Kim, H., & Feamster, N. (2013). Improving network management with software defined networking. IEEE Communications magazine, 114-119
[7] Das, T., Abu, H., Shukla, R., Sengupta, S., & Arslan, E. (2022). University of Nevada - Reno Intrusion Detection Dataset (UNR-IDD). TechRxiv.
[8] Emre, U., Sonali, S.-B., & Rattikorn, H. (2018). Towards Prediction of Security Attacks on Software Defined Networks. Interntional Conference on Big Data
[9] Marwin, Z., Florian, E., & Samuel, K. (2021). Machine Learning Model Update Strategies for Hard Disk Drive Failure Prediction. 20th IEEE International Conference on Machine Learning and Applications. IEEE
[10] Nunes, B., Mendonca, M., Nguyen, N., Obraczka, K., & Turletti, T. (2014). A survey of software-defined networking: past, present and future of programmable networks. Communications Surveys and Tutorials, IEEE, 1617-1634.
[11] Rudolf, F., Peter, H., Peter, G., Gabor, A., Denes, B., & Tibor, G. (2019). Challenging Machine Learning algorithms in Predicting Vulnerable Javascript Functions. 7th International Workshop on Realizing Artificial Intelligence synergies in Software Engineering. Szeged: University of Szeged.
[12] Rista, A., Ajdari, J., & Zenuni, X. (2020). Predicting and analyzing Absenteeism at Workplace Using Machine Learning Algorithms. 43rd International Convention on Information, Communication and Electronic Technology , 485-490
[13] Saurav, N., Faheem, Z., Zasimer, D., Eric, W., & Baijian, Y. (2016). Predicting Network attack Patterns in SDN using Machine Learning Approach. IEEE Conference on Network Function Virtualization and Software Defined Networks. New York
[14] Sommer, V. (2014). Anomaly Detection in SDN Control Plane. Munich: Master's thesis, Technical University of Munich
[15] Ullman, S., Poggio, T., Harari, D., Zysman, D., & Seibert, D. (2020). Unsupervised learning - Clustering. Center for Brains, Minds and Machines
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Published
2023-01-08
Issue
Section
Electrotechnical and Computer Engineering
