(Self) Driving Under the Influence: Intoxicating Adversarial Network Inputs
Abstract
Traditional network control planes can be slow and require manual tinkering from operators to change their behavior. There is thus great interest in a faster, data-driven approach that uses signals from real-time traffic instead. However, the promise of fast and automatic reaction to data comes with new risks: malicious inputs designed towards negative outcomes for the network, service providers, users, and operators.
Adversarial inputs are a well-recognized problem in other areas; we show that networking applications are susceptible to them too. We characterize the attack surface of data-driven networks and examine how attackers with different privileges—from infected hosts to operator-level access—may target network infrastructure, applications, and protocols. To illustrate the problem, we present case studies with concrete attacks on recently proposed data-driven systems.
Our analysis urgently calls for a careful study of attacks and defenses in data-driven networking, with a view towards ensuring that their promise is not marred by oversights in robust design.
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BibTex
@inproceedings{meier2019driving,
author = {Meier, Roland and Holterbach, Thomas and Keck, Stephan and St{\"{a}}hli, Matthias and Lenders, Vincent and Singla, Ankit and Vanbever, Laurent},
title = {{(Self) Driving Under the Influence: Intoxicating Adversarial Network Inputs}},
booktitle = {Proceedings of the 18th ACM Workshop on Hot Topics in Networks (HotNets '19)},
address = {Princeton, NJ, USA},
year = 2019,
month = nov,
publisher = {Association for Computing Machinery},
doi = {10.1145/3365609.3365850},
url = {https://doi.org/10.1145/3365609.3365850}
}Research Collection: 20.500.11850/384537