2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · 2020 · 52 citations · 7 references
ComputingComputer ScienceCybersecurityComputer SecurityMachine LearningNetwork SecurityDeep LearningDenial-of-service AttacksIntrusion DetectionNetwork Traffic AnalysisSoftware-defined NetworkingThreat DetectionAttack DetectionControl Plane SeparationConvolutional Neural NetworksData Plane SeparationDdos DetectionDeep Learning-based DetectionInformation EntropySoftware-defined Networking Security
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.
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Deep learning approach for Network Intrusion Detection in Software Defined Networking
Tuan Anh Tang, Lotfi Mhamdi, Des McLernon et al. · 2016 · 800 citations
DeepDefense: Identifying DDoS Attack via Deep Learning
Xiaoyong Yuan, Chuanhuang Li, Xiaolin Li · 2017 · 390 citations