Visible to the public Machine-Learning Based DDOS Attack Classifier in Software Defined Network

TitleMachine-Learning Based DDOS Attack Classifier in Software Defined Network
Publication TypeConference Paper
Year of Publication2020
AuthorsKyaw, A. T., Oo, M. Zin, Khin, C. S.
Conference Name2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)
Keywordscentralized control, centralized controller, composability, Computer crime, computer network management, computer network security, DDoS, DDoS Attacks, feature extraction, IP networks, learning (artificial intelligence), linear SVM, machine learning algorithms, machine-learning based DDOS attack classifier, network administrators, normal attack traffic, pattern classification, polynomial SVM, Predictive Metrics, programmable capability, pubcrawl, Resiliency, RYU, RYU SDN controller, scapy, SDN, SDN architecture, SDN network, security of data, service attacks, Software Defined Network, software defined networking, Support vector machines, SVM, Switches, telecommunication security, telecommunication traffic
AbstractDue to centralized control and programmable capability of the SDN architecture, network administrators can easily manage and control the whole network through the centralized controller. According to the SDN architecture, the SDN controller is vulnerable to distributed denial of service (DDOS) attacks. Thus, a failure of SDN controller is a major leak for security concern. The objectives of paper is therefore to detect the DDOS attacks and classify the normal or attack traffic in SDN network using machine learning algorithms. In this proposed system, polynomial SVM is applied to compare to existing linear SVM by using scapy, which is packet generation tool and RYU SDN controller. According to the experimental result, polynomial SVM achieves 3% better accuracy and 34% lower false alarm rate compared to Linear SVM.
Citation Keykyaw_machine-learning_2020