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).
Design and Analysis of Robust Resilient Diffusion over Multi-Task Networks Against Byzantine Attacks
Southwest Petroleum University（西南石油大学）；Pontifical Catholic University of Rio de Janeiro（里约热内卢天主教大学）；University of York（约克大学）；Southwest University of Science and Technology（西南科技大学）
Stochastic-HMDs: Adversarial Resilient Hardware Malware Detectors through Voltage Over-scaling
George Mason University（乔治梅森大学）；Dhaka University of Engineering & Technology（达卡工程技术大学）；Universite Polytechnique Hauts-De-France（上法兰西理工大学）；CNRS（法国国家科学研究中心）；IEMN（微电子与纳米技术研究所）
Generalization Error Analysis for Attack-Free and Byzantine-Resilient Decentralized Learning with Data Heterogeneity
School of Computer Science and Engineering, Sun Yat-Sen University（中山大学计算机科学与工程学院）；College of Computer, National University of Defense Technology（国防科技大学计算机学院）
