APPLICATION OF MACHINE LEARNING TECHNIQUES ON THE PROBLEM OF CLASSIFICATION OF DIFFERENT SCENARIOS OF BOTNET ATTACKS
DOI:
https://doi.org/10.24867/32BE12MladenovicKeywords:
Machine learning, classification, botnet, cyber securityAbstract
Cyber attacks are becoming part of everyday life, and their sophistication is increasing with frequency. This is precisely why more progress and continuous innovation in defense strategies is needed. Traditional methods of intrusion detection and deep packet inspection, although still widely used and recommended, are no longer sufficient to meet the demands of growing security threats.
References
[1] Claise, Benoit. 2004. Cisco systems netflow services export version.
[2] Choras, Rafal Kozik and Michal. 2017. “Pattern Extraction Algorithm for NetFlow-Based Botnet Activities Detection". Security and Communication Networks 2017.
[3] Cynthia Wagner, Jerome Francois, Thomas Engel, et al. 2011. Machine learning approach for ip-flow record anomaly detection. International Conference on Research in Networking, Springer.
[4] Wikipedia. Accessed 09 01, 2024. https ://en .wikipedia .org/wiki/Machine_learning
[5] Matija Stevanovic, Jens Myrup Pedersen. 2014. "An efficient flow-based botnet detection using supervised machine learning." 2014 international conference on computing, networking and 797-801.
[6] David Santana, Shan Suthaharan, Somya Mohanty. 2018. "What we learn from learning-Understanding capabilities and limitations of machine learning in botnet attacks." arXiv preprint
[7] Kozik Rafal, Michal Choras, and Jorg Keller. 2019. "Balanced Efficient Lifelong Learning (B-ELLA)
[8] P Fruehwirt, S Schrittwieser, i ER Weippl. 2014. "Using machine learning techniques for traffic classification [9] Jiangpan Hou et. 2018. "Machine Learning Based DDos Detection Through NetFlow Analysis."
