| Title | A DDoS Attack Detection Method Based on Information Entropy and Deep Learning in SDN |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Wang, L., Liu, Y. |
| Conference Name | 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) |
| Date Published | jun |
| Keywords | composability, computer network security, control plane, convolutional neural nets, convolutional neural network model, data plane, DDoS attack detection, DDoS attack detection method, DDoS attack traffic, DDoS Attacks, Deep Learning, Entropy, fine-grained packet-based detection, Human Behavior, information entropy, information entropy detection, learning (artificial intelligence), Metrics, pubcrawl, resilience, Resiliency, SDN, Software Defined Network, software defined networking, telecommunication traffic |
| Abstract | Software Defined Networking (SDN) decouples the control plane and the data plane and solves the difficulty of new services deployment. However, the threat of a single point of failure is also introduced at the same time. The attacker can launch DDoS attacks towards the controller through switches. In this paper, a DDoS attack detection method based on information entropy and deep learning is proposed. Firstly, suspicious traffic can be inspected through information entropy detection by the controller. Then, fine-grained packet-based detection is executed by the convolutional neural network (CNN) model to distinguish between normal traffic and attack traffic. Finally, the controller performs the defense strategy to intercept the attack. The experiments indicate that the accuracy of this method reaches 98.98%, which has the potential to detect DDoS attack traffic effectively in the SDN environment. |
| DOI | 10.1109/ITNEC48623.2020.9085007 |
| Citation Key | wang_ddos_2020 |
A DDoS Attack Detection Method Based on Information Entropy and Deep Learning in SDN