The distributed denial of service (DDoS) attack is detrimental to the industrial Internet of things (IIoT) as it triggers severe resource starvation on networked objects. Recent dynamics demonstrate that it is a highly profitable business for attackers using botnets. Current centralized mitigation solutions concentrate on detection and mitigation at a victim's side, paying inadequate attention to hacking costs and the collaboration of defenders. Thus, we propose the federated learning empowered mitigation architecture (FLEAM) to advocate joint defense, incurring a higher hacking expense. FLEAM combines FL and fog computing to reduce mitigation time and improve detection accuracy, enabling defenders to jointly combatting botnets. Our comprehensive evaluations showcase that the attacking expense incurred is 2.5 times higher, the mitigation delay is about 72% lower, and the accuracy is 47% greater on average than classic solutions.
翻译:分布式拒绝服务(DDoS)攻击有害于工业互联网,因为它在网络物体上引发严重的资源饥饿。最近的动态表明,这是一个对使用肉网袭击者来说非常有利可图的行业。目前的集中缓解解决方案集中在受害者一方的检测和缓解,对黑客成本和维权者的合作重视不够。因此,我们建议联合学习授权缓解架构(FLEAM)倡导联合防御,导致更高的黑客成本。 FLEAM将FL和雾计算结合起来,以减少缓解时间,提高检测准确性,使维权者能够联合打击肉网。我们的全面评估显示,袭击成本是受害者一方的2.5倍,减缓延迟率大约低72%,平均准确率比传统解决方案高47%。