PDL Abstract

SurgeProtector: Mitigating Temporal Algorithmic Complexity Attacks using Adversarial Scheduling

SIGCOMM ’22, August 22–26, 2022, Amsterdam, Netherlands.

Nirav Atre, Hugo Sadok, Erica Chiang, Weina Wang, Justine Sherry

Carnegie Mellon University

http://www.pdl.cmu.edu/

Denial-of-Service (DoS) attacks are the bane of public-facing network deployments. Algorithmic complexity attacks (ACAs) are a class of DoS attacks where an attacker uses a small amount of adversarial traffic to induce a large amount of work in the target system, pushing the system into overload and causing it to drop packets from innocent users. ACAs are particularly dangerous because, unlike volumetric DoS attacks, ACAs don’t require a significant network bandwidth investment from the attacker. Today, network functions (NFs) on the Internet must be designed and engineered on a case-by-case basis to mitigate the debilitating impact of ACAs. Further, the resulting designs tend to be overly conservative in their attack mitigation strategy, limiting the innocent traffic that the NF can serve under common-case operation.

In this work, we propose a more general framework to make NFs resilient to ACAs. Our framework, SurgeProtector, uses the NF’s scheduler to mitigate the impact of ACAs using a very traditional scheduling algorithm:Weighted Shortest Job First (WSJF). To evaluate SurgeProtector, we propose a new metric of vulnerability called the Displacement Factor (DF), which quantifies the ‘harm per unit effort’ that an adversary can inflict on the system.We provide novel, adversarial analysis of WSJF and show that any system using this policy has a worst-case DF of only a small constant, where traditional schedulers place no upper bound on the DF. Illustrating that SurgeProtector is not only theoretically, but practically robust, we integrate SurgeProtector into an open source intrusion detection system (IDS). Under simulated attack, the SurgeProtector- augmented IDS suffers 90-99% lower innocent traffic loss than the original system.

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