| Title: | GS5-3 Rule based Intrusion Detection System by Using Statistical Flow Analysis Technique for Software Defined Network |
|---|---|
| Publication: | ICAROB2019 |
| Volume: | 24 |
| Pages: | 687-692 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2019.GS5-3 |
| Author(s): | Mahnoor Ejaz, Osama Sohail, Talha Naqash, Zain ul Abideen, Sajjad Hussain Shah |
| Publication Date: | January 10, 2019 |
| Keywords: | Network Security, Software Defined Networking (SDN), Intrusion Detection System (IDS), Network Monitoring, Network traffic flows |
| Abstract: | Network Security is a vast field making progress around the globe very fast. In every progressing year, developers have implemented different tools, which include Intrusion detection systems. Nowadays Intrusion Detection System (IDS) is one of the popular tools, which are drawing the attention of many researchers. Applying it in Software Defined Networking (SDN) facilitates network management and enables to enhance the productivity of network monitoring. Classifying packets based on their statistics and separating the forward process of network packets from the routing process is the main challenge. In this paper, rule-based classification is done in order to differentiate between viruses and normal packets. Statistical analysis of different network traffic flows are done through which segregation is made and intrusion is detected in Software Defined Networking. The proposed system is experimentally tested on UNB ISCX datasets |
| PDF File: | https://alife-robotics.co.jp/members2019/icarob/data/html/data/GS_pdf/GS5/GS5-3.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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