A DDoS attack detection method based on SVM and K-nearest neighbour in SDN environment. (24th November 2020)

• Record Type: Journal Article
• Authors: Ma, Zhaohui; Li, Bohong
• Is Part Of: International journal of computational science and engineering. Volume 23:Number 3(2020)
• Page Start: 224
• Page End: 234
• Publication Date: 2020-11-24
• Subjects: software defined network -- SDN -- controller -- detecting method -- DDoS attack -- K-nearest neighbour -- KNN -- support vector machine -- SVM; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

This paper presents a detection method for DDoS attack in SDN based on K-nearest neighbour (KNN) algorithm and support vector machine (SVM) algorithm. This method makes use of the characteristics of SDN centralised control, collects flow characteristic information efficiently, classifies the flow, screens out the attack flow, and determines whether the system is attacked or not. Experiments show that the resource consumption rate of this model is only 11% when detecting DDOS attack. Meanwhile, the accuracy rate exceeded 99%.