With the advent of Software Defined Networks (SDNs), there has been a rapid advancement in the area of cloud computing. It is now scalable, cheaper, and easier to manage. However, SDNs are more prone to security vulnerabilities as compared to legacy systems. Therefore, machine-learning techniques are now deployed in the SDN infrastructure for the detection of malicious traffic. In this paper, we provide a systematic benchmarking analysis of the existing machine-learning techniques for the detection of malicious traffic in SDNs. We identify the limitations in these classical machine-learning based methods, and lay the foundation for a more robust framework. Our experiments are performed on a publicly available dataset of Intrusion Detection Systems (IDSs).
@article{arxiv.1910.00817,
title = {Machine-Learning Techniques for Detecting Attacks in SDN},
author = {Mahmoud Said Elsayed and Nhien-An Le-Khac and Soumyabrata Dev and Anca Delia Jurcut},
journal= {arXiv preprint arXiv:1910.00817},
year = {2019}
}
Comments
Published in 2019 IEEE 7th International Conference on Computer Science and Network Technology