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CPSS LR-DDoS Detection and Defense in Edge Computing Utilizing DCNN Q-Learning
2020
IEEE Access
Existing intrusion detection and defense models for CPSS (Cyber-Physical-Social Systems) are based on analyzing the static intrusion characteristics, which cannot effectively detect large-scale Low-Rate Denial-of-Service (LR-DDoS) attacks, especially in the edge environment. In this paper, we firstly explore and enhance Mirai botnet to a sophisticated multi-targets low-rate TCP attack network, which makes edge LR-DDoS more powerful and obfuscates their activity. And then, we develop a novel
doi:10.1109/access.2020.2976706
fatcat:7azagv5yw5ddlcduwuwvf6aeiu