Security challenges in internet of things: Distributed denial of service attack detection using support vector machine‐based expert systems. (21st February 2020)

• Record Type: Journal Article
• Authors: Mubarakali, Azath; Srinivasan, Karthik; Mukhalid, Reham; Jaganathan, Subash C. B.; Marina, Ninoslav
• Is Part Of: Computational intelligence. Volume 36:Number 4(2020)
• Page Start: 1580
• Page End: 1592
• Publication Date: 2020-02-21
• Subjects: distributed denial of service (DDoS) attack -- internet of things (IoT) -- software‐defined network (SDN) -- support vector machine (SVM); Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12293
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: The rapid development of internet of things (IoT) is to be the next generation of the IoT devices are a simple target for attackers due to the lack of security. Attackers can easily hack the IoT devices that can be used to form botnets, which can be used to launch distributed denial of service (DDoS) attack against networks. Botnets are the most dangerous threat to the security systems. Software‐defined networking (SDN) is one of the developing filed, which introduce the capacity of dynamic program to the network. Use the flexibility and multidimensional characteristics of SDN used to prevent DDoS attacks. The DDoS attack is the major attack to the network, which makes the entire network down, so that normal users might not avail the services from the server. In this article, we proposed the DDoS attack detection model based on SDN environment by combining support vector machine classification algorithm is used to collect flow table values in sampling time periods. From the flow table values, the five‐tuple characteristic values extracted and based on it the DDoS attack can be detected. Based on the experimental results, we found the average accuracy rate is 96.23% with a normal amount of traffic flow. Proposed research offers a better DDoS detection rate on SDN.