论文标题
FLEAM：联合学习的授权建筑，以减轻工业物联网中的DDO
FLEAM: A Federated Learning Empowered Architecture to Mitigate DDoS in Industrial IoT
论文作者
论文摘要
分布式拒绝服务（DDOS）攻击对工业互联网（IIOT）有害，因为它触发了网络对象的严重资源饥饿。最近的动态表明，对于使用僵尸网络的攻击者来说，这是一项盈利的业务。当前的集中缓解解决方案集中于受害者一方的检测和缓解措施，这不足以关注黑客成本和捍卫者的合作。因此，我们建议联邦学习的授权缓解架构（FLEAM）提倡共同防御，从而产生更高的黑客费用。 Fleam结合了FL和FOG计算，以减少缓解时间并提高检测准确性，从而使后卫能够共同对抗僵尸网络。我们的全面评估表明，产生的攻击费用是2.5倍，缓解延迟的延迟低约72％，并且准确性平均高47％。
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.
