2017/10/12 by Rui Zhang, Quanyan Zhu, Zhang, Rui +1 · 1 citation
Computer Science · Engineering · Mathematics · #Adversarial Robustness in Machine Learning #Adversarial system #Adversary #Artificial intelligence #Computer Science and Game Theory (cs.GT) #Computer science #Computer security #Distributed Sensor Networks and Detection Algorithms #Distributed computing #FOS: Computer and information sciences #Human–computer interaction #Machine Learning (stat.ML) #Machine learning #Mathematical optimization #Nash equilibrium #Outcome (game theory) #Resilience (materials science) #Scalability #Smart Grid Security and Resilience #Threat model #cs.GT #stat.ML
paper · pdf · doi:10.48550/arxiv.1710.04677
published in arXiv (Cornell University) (Cornell University)
arxiv created 2017/10/12 · openalex publication_date 2017/10/12 · arxiv updated 2017/10/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
With a large number of sensors and control units in networked systems, distributed support vector machines (DSVMs) play a fundamental role in scalable and efficient multi-sensor classification and prediction tasks. However, DSVMs are vulnerable to adversaries who can modify and generate data to deceive the system to misclassification and misprediction. This work aims to design defense strategies for DSVM learner against a potential adversary. We establish a game-theoretic framework to capture the conflicting interests between the DSVM learner and the attacker. The Nash equilibrium of the game allows predicting the outcome of learning algorithms in adversarial environments, and enhancing the resilience of the machine learning through dynamic distributed learning algorithms. We show that the DSVM learner is less vulnerable when he uses a balanced network with fewer nodes and higher degree. We also show that adding more training samples is an efficient defense strategy against an attacker. We present secure and resilient DSVM algorithms with verification method and rejection method, and show their resiliency against adversary with numerical experiments.