Menghao Zhang (Tsinghua University), Guanyu Li (Tsinghua University), Shicheng Wang (Tsinghua University), Chang Liu (Tsinghua University), Ang Chen (Rice University), Hongxin Hu (Clemson University), Guofei Gu (Texas A&M University), Qi Li (Tsinghua University), Mingwei Xu (Tsinghua University), Jianping Wu (Tsinghua University)

Distributed Denial-of-Service (DDoS) attacks have become a critical threat to the Internet. Due to the increasing number of vulnerable Internet of Things (IoT) devices, attackers can easily compromise a large set of nodes and launch highvolume DDoS attacks from the botnets. State-of-the-art DDoS defenses, however, have not caught up with the fast development of the attacks. Middlebox-based defenses can achieve high performance with specialized hardware; however, these defenses incur a high cost, and deploying new defenses typically requires a device upgrade. On the other hand, software-based defenses are highly flexible, but software-based packet processing leads to high performance overheads. In this paper, we propose Poseidon, a system that addresses these limitations in today’s DDoS defenses. It leverages emerging programmable switches, which can be reconfigured in the field without additional hardware upgrade. Users of Poseidon can specify their defense strategies in a modular fashion in the form of a set of defense primitives; this can be further customized easily for each network and extended to include new defenses. Poseidon then maps the defense primitives to run on programmable switches—and when necessary, on server software—for effective defense. When attacks change, Poseidon can reconfigure the underlying defense primitives to respond to the new attack patterns. Evaluations using our prototype demonstrate that Poseidon can effectively defend against highvolume attacks, easily support customization of defense strategies, and adapt to dynamic attacks with low overheads.

View More Papers

Deceptive Previews: A Study of the Link Preview Trustworthiness...

Giada Stivala (CISPA Helmholtz Center for Information Security), Giancarlo Pellegrino (CISPA Helmholtz Center for Information Security)

Read More

Unicorn: Runtime Provenance-Based Detector for Advanced Persistent Threats

Xueyuan Han (Harvard University), Thomas Pasquier (University of Bristol), Adam Bates (University of Illinois at Urbana-Champaign), James Mickens (Harvard University), Margo Seltzer (University of British Columbia)

Read More

SurfingAttack: Interactive Hidden Attack on Voice Assistants Using Ultrasonic...

Qiben Yan (Michigan State University), Kehai Liu (Chinese Academy of Sciences), Qin Zhou (University of Nebraska-Lincoln), Hanqing Guo (Michigan State University), Ning Zhang (Washington University in St. Louis)

Read More