计算机应用研究2026，Vol.43Issue(2)：596-603,8.DOI:10.19734/j.issn.1001-3695.2025.06.0219
面向SDN流表多模态感知与DRL协同防御DDoS方法
SDN flow table multi-modal perception and DRL collaborative defense against DDoS method
摘要
Abstract
The centralized control architecture of SDN enhances management efficiency while posing risks of DDoS attacks.Addressing the challenges of traditional detection methods in handling covert attack behaviors within large-scale dynamic traffic and the tendency to mistakenly block short-term high-concurrency normal traffic,this paper proposed a DDoS defense system based on multi-modal deep reinforcement learning.This system achieved a dynamic balance between detection accuracy and re-source efficiency by integrating spatio-temporal feature decoupling and intelligent decision optimization.It maximized the avoi-dance of denial of service for non-attack traffic when resources are abundant.Experimental results show that the attack detec-tion accuracy rate averages 99.61％,with a false positive rate not exceeding 0.5％.This system reduces false positives for le-gitimate traffic while maintaining high accuracy,thereby ensuring the quality of network services during the defense process.关键词
软件定义网络/分布式拒绝服务攻击/对抗深度强化学习网络/张量分解Key words
software-defined networking(SDN)/distributed denial-of-service(DDoS)attack/adversarial deep reinforce-ment learning network/tensor decomposition分类
信息技术与安全科学引用本文复制引用
徐泽鹏,舒兆港,陈淑武,涂强,庄涛..面向SDN流表多模态感知与DRL协同防御DDoS方法[J].计算机应用研究,2026,43(2):596-603,8.基金项目
福建省福厦泉协同创新平台项目(2025E3012) （2025E3012）
福建省高校产学研合作项目(2024H6007) （2024H6007）
福建省高校产学研联合创新项目(2024H6030) （2024H6030）
