Conference Paper


A hierarchical Intrusion Detection System using support vector machine for SDN network in cloud data center

Abstract

Software-Defined Networks (SDN) has emerged as a dominant programmable network architecture for cloud based data centers. Its centralised programmable control plane decoupled from the data plane with a global view of the network state provides new opportunities to implement innovate security mechanisms. This research leverages this features of SDN and presents the architecture of a hierarchical and lightweight Intrusion Detection System (IDS) for software enabled networks by exploiting the concept of SDN flows. It combines advantages of a flow-based IDS and a packet-based IDS in order to provide a high detection rate without degrading network performances. The flow-based IDS uses an anomaly detection algorithm based on Support Vector Machines (SVM) trained with DARPA Intrusion Detection Dataset . This first line of defence detects any intrusions on the network. When an attack is detected, the malicious flow is mirrored to a packet-based IDS, for further examination and actions. The results show that this scheme provides good detection rates and performances with minimal extra overhead.

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Authors

Basu, Kashinath
Younas, Muhammad
Schueller, Quentin
Patel, Mohit
Ball, Frank

Oxford Brookes departments

Faculty of Technology, Design and Environment\School of Engineering, Computing and Mathematics

Dates

Year of publication: 2019
Date of RADAR deposit: 2018-09-13


Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License


Related resources

This RADAR resource is the Accepted Manuscript of A hierarchical Intrusion Detection System using support vector machine for SDN network in cloud data center

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