Resilient linear classification an approach to deal with attacks on training data

• Published in: 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems (ICCPS) pp. 155 - 164
• Main Authors: Park, Sangdon, Weimer, James, Lee, Insup
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 18.04.2017
• Series: ACM Other Conferences
• Subjects: Classification algorithms; Computer systems organization > Embedded and cyber-physical systems; Computing methodologies > Machine learning > Learning paradigms > Supervised learning > Supervised learning by classification; Computing methodologies > Machine learning > Learning settings > Batch learning; Cyber-physical systems; linear classification; Measurement; Resilience; Robot sensing systems; Security; Security and privacy > Software and application security > Domain-specific security and privacy architectures; Training data; training data attacks; cyber-physical systems; training data attacks; linear classification
• ISBN: 9781450349659; 145034965X
• DOI: 10.1145/3055004.3055006

Data-driven techniques are used in cyber-physical systems (CPS) for controlling autonomous vehicles, handling demand responses for energy management, and modeling human physiology for medical devices. These data-driven techniques extract models from training data, where their performance is often analyzed with respect to random errors in the training data. However, if the training data is maliciously altered by attackers, the effect of these attacks on the learning algorithms underpinning data-driven CPS have yet to be considered. In this paper, we analyze the resilience of classification algorithms to training data attacks. Specifically, a generic metric is proposed that is tailored to measure resilience of classification algorithms with respect to worst-case tampering of the training data. Using the metric, we show that traditional linear classification algorithms are resilient under restricted conditions. To overcome these limitations, we propose a linear classification algorithm with a majority constraint and prove that it is strictly more resilient than the traditional algorithms. Evaluations on both synthetic data and a real-world retrospective arrhythmia medical case-study show that the traditional algorithms are vulnerable to tampered training data, whereas the proposed algorithm is more resilient (as measured by worst-case tampering).