Detection of non-periodic low-rate denial of service attacks in software defined networks using machine learning

Detection of non-periodic low-rate denial of service attacks in software defined networks using machine learning

연구 분야: Networking

학회: International Journal of Information Technology

In this paper, we propose a novel approach to detect non-periodic Low-rate Denial of Service attacks in Software Defined Networks using Machine Learning algorithms. Low-rate Denial of Service attacks are a type of cyber-attack that aim to disrupt network services by sending low-rate traffic to the target system. These attacks can be difficult to detect as they do not exhibit the same characteristics as traditional high-rate Denial of Service attacks. However, despite their low-rate nature, Low-rate Denial of Service attacks can still have significant harmful effects on network performance and availability. Our approach leverages the flexibility and programmability of Software Defined Networks to collect network traffic data and apply Machine Learning algorithms to detect non-periodic Low-rate Denial of Service attacks in real-time. We evaluate our approach using a simulated Software Defined Networks environment and demonstrate its effectiveness in accurately detecting non-periodic Low-rate Denial of Service attacks.