Machine-Learning Techniques for Detecting Attacks in SDN

被引：0

作者：

Elsayed, Mahmoud Said ^{[1
]}

Nhien-An Le-Khac ^{[1
]}

Dev, Soumyabrata ^{[1
,2
,3
]}

Jurcut, Anca Delia ^{[1
,2
]}

机构：

[1] Univ Coll Dublin, Dublin, Ireland

[2] Beijing Dublin Int Coll, Beijing, Peoples R China

[3] ADAPT SFI Res Ctr, Dublin, Ireland

来源：

PROCEEDINGS OF 2019 IEEE 7TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT 2019) | 2019年

关键词：

Software Defined Networking; Intrusion Detection Systems; Denial of Service (DoS); machine learning;

D O I：

10.1109/iccsnt47585.2019.8962519

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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 vulner-abilities 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).

引用

页码：277 / 281

页数：5

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