Distributed Denial of Service (DDoS) attacks are one of the most harmful threats in today's Internet, disrupting the availability of essential services. The challenge of DDoS detection is the combination of attack approaches coupled with the volume of live traffic to be analysed. In this paper, we present a practical, lightweight deep learning DDoS detection system called LUCID, which exploits the properties of Convolutional Neural Networks (CNNs) to classify traffic flows as either malicious or benign. We make four main contributions; (1) an innovative application of a CNN to detect DDoS traffic with low processing overhead, (2) a dataset-agnostic preprocessing mechanism to produce traffic observations for online attack detection, (3) an activation analysis to explain LUCID's DDoS classification, and (4) an empirical validation of the solution on a resource-constrained hardware platform. Using the latest datasets, LUCID matches existing state-of-the-art detection accuracy whilst presenting a 40x reduction in processing time, as compared to the state-of-the-art. With our evaluation results, we prove that the proposed approach is suitable for effective DDoS detection in resource-constrained operational environments.
When Time Meets Space: Entropy Integration and Dynamic Threshold for Adaptive DDoS Detection in SDN
National Engineering Research Center of Mobile Network Technologies, Beijing University of Posts and Telecommunications（北京邮电大学移动通信国家重点实验室）；Vanderbilt University（范德堡大学）；Louisiana State University（路易斯安那州立大学）
Teacher-free Latent Self-distillation and Class-separable Representations for Lightweight IoT Attack Detection
School of Electrical and Data Engineering, the University of Technology Sydney（电气与数据工程学院，悉尼大学技术学院）；Computer Science Department, Institute of Information and Communication Technology, Le Quy Don Technical University（计算机科学系，信息与通信技术研究所，吕公独技术大学）；University of Engineering and Technology, Vietnam National University（工程与技术大学，越南国家大学）
Collaborative Zone-Adaptive Zero-Day Intrusion Detection for IoBT
School of Computing and Information Systems, The University of Melbourne（墨尔本大学计算与信息系统学院）；School of Systems & Computing, University of New South Wales（新南威尔士大学系统与计算学院）
