Software Defined Networking Based Detection of DDoS Attacks

Abstract

Software-defined networking (SDN) separates network management from data-traffic routes. More businesses are adopting it because of its flexibility, adaptability, and ability to improve traffic movement. SDN, or security-by-design, might be a desirable alternative for securing networks. While there have been many advancements in SDN technology, it is still susceptible to DDoS assaults. The growing frequency and scope of DDoS attacks pose a threat to network security, despite the availability of various methods for detecting and countering such attacks. There are two main methods to spot a distributed denial of service (DDoS) attack: signature recognition and abnormality detection. When personal characteristics such as fingerprints or iris scans are used to verify identity. Anomaly-based detection, which relies on network behavior, employs machine learning methods. We present a strategy for SDNs to identify DDoS assaults in this article. In the proposed architecture, DDoS attacks are detected using the Advanced Support Vector Machine (ASVM) technique. When compared to the SVM method, ASVM has the benefit of significantly less testing and training time. To evaluate the effectiveness of the suggested system, we use the Hierarchical Task Analysis (HTA) method of measuring human error.