ISPITIVANJE ALGORITAMA MAŠINSKOG UČENJA ZA PREDVIĐANJE NAPADA NA SOFTVERSKI DEFINISANE MREŽE
Ključne reči:
Veliki podaci, mašinsko učenje, mreže definisane softverom, sajber napadi, detekcija upada
Apstrakt
Sa razvojem tehnologije obrade velikih podataka se razvijaju sistemi detekcije upada i anomalija. Malo istraživanja je sprovedeno u oblasti mreža definisanih softverom. Ovaj rad ispituje upotrebu tehnologija obrade velikih podataka za pronalazak sajber napada u softverski-definisanim mrežama. U radu su iskorišćeni algoritmi mašinskog učenja – stabla odlučivanja, nasumične šume i Naivni Bajes – za detekciju upada nad javno dostupnim skupom podataka. Postignuta preciznost je 96.7%.
Reference
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[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
Objavljeno
2023-01-08
Sekcija
Elektrotehničko i računarsko inženjerstvo
