Detecting Saturation Attacks Based on Self-Similarity of OpenFlow Traffic
| Autor: | Zhiyuan Li, Samer Khamaiseh, Weijia Xing, Dianxiang Xu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
OpenFlow
Computer Networks and Communications Network packet Computer science business.industry ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS 020206 networking & telecommunications 02 engineering and technology IP address spoofing Flooding (computer networking) Internet Control Message Protocol 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering SYN flood Software-defined networking business Traffic generation model Computer network |
| Zdroj: | IEEE Transactions on Network and Service Management. 17:607-621 |
| ISSN: | 2373-7379 |
| DOI: | 10.1109/tnsm.2019.2959268 |
| Popis: | As a new networking paradigm, Software-Defined Networking (SDN) separates data and control planes to facilitate programmable functions and improve the efficiency of packet delivery. Recent studies have shown that there exist various security threats in SDN. For example, a saturation attack may disturb the normal delivery of packets and even make the SDN system out of service by flooding the data plane, the control plane, or both. The existing research has focused on saturation attacks caused by SYN flooding. This paper presents an anomaly detection method, called SA-Detector, for dealing with a family of saturation attacks through IP spoofing, ICMP flooding, UDP flooding, and other types of TCP flooding, in addition to SYN flooding. SA-Detector builds upon the study of self-similarity characteristics of OpenFlow traffic between the control and data planes. Our work has shown that the normal and abnormal traffic flows through the OpenFlow communication channel have different statistical properties. Specifically, normal OpenFlow traffic has a low self-similarity degree whereas the occurrences of saturation attacks typically imply a higher degree of self-similarity. Therefore, SA-Detector exploits statistical results and self-similarity degrees of OpenFlow traffic, measured by Hurst exponents, for anomaly detection. We have evaluated our approach in both physical and simulation SDN environments with various time intervals, network topologies and applications, Internet protocols, and traffic generation tools. For the physical SDN environment, the average accuracy of detection is 97.68% and the average precision is 94.67%. For the simulation environment, the average accuracy is 96.54% and the average precision is 92.06%. In addition, we have compared SA-Detector with the existing saturation attack detection methods in terms of the aforementioned performance metrics and controller’s CPU utilization. The experiment results indicate that SA-Detector is effective for the detection of saturation attacks in SDN. |
| Databáze: | OpenAIRE |
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