Performance and Features: Mitigating the Low-Rate TCP-Targeted DoS Attack via SDN

Software-Defined Networking (SDN) is an emerging network architecture. The decoupled data and control plane provides programmability for efficient network management. However, the centralized control mode of SDN also exposes unique vulnerabilities. Low-rate Denial of Service (LDoS) has a lower attack rate than ordinary DDoS attacks with the characteristics of periodicity and concealment, which is among one of the severe threats to SDN. In this paper, we propose a lightweight, real-time framework Performance and Features (P&F) to detect and mitigate LDoS attacks with SDN. We implement LDoS attacks in SDN, extract traffic features with OpenFlow, and classify the features into two categories. By analyzing the performance (P) of normal traffic under attack state, P&F determines whether LDoS attacks take effect based on machine learning. Meanwhile, P&F tries to locate attack sources and victims according to flow features (F) of LDoS attacks based on time-frequency analysis. According to detection and locating results, P&F sets corresponding mitigation schemes. Experimental results show that P&F has a high detection rate and low false positive rate for detecting LDoS attacks. P&F can deploy on controllers to achieve real-time attack detection and mitigation with low system cost, which can defend against LDoS attacks effectively.

Automated summary

This paper proposes a lightweight, real-time framework P&F for detecting and mitigating LDoS attacks in SDN. By analyzing traffic features and implementing machine learning, P&F is able to efficiently detect LDoS attacks and locate their sources and victims. Experimental results show that P&F achieves high detection rate and low false positive rate, effectively defending against LDoS attacks.