Machine-Learning Techniques for Detecting Attacks in SDN

Authors: Mahmoud Said Elsayed, Nhien-An Le-Khac, Soumyabrata Dev, Anca Delia Jurcut
Published: 2019-10-02

Source: https://arxiv.org/abs/1910.00817

PDF: https://arxiv.org/pdf/1910.00817

AIにより推定されたラベル

攻撃の評価 機械学習アルゴリズム サイバー脅威

※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
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Abstract

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).