论文标题
分布式机器学习的安全：一种设计安全DSVM的游戏理论方法
Security of Distributed Machine Learning: A Game-Theoretic Approach to Design Secure DSVM
论文作者
论文摘要
分布式机器学习算法在处理大型网络的大量数据集中起着重要作用。但是，越来越多地依赖机器学习信息和通信技术（ICT）使其本质上容易受到网络威胁的影响。这项工作旨在开发安全的分布式算法，以保护学习免受数据中毒和网络攻击的影响。我们建立了一个游戏理论框架，以捕获使用分布式支持向量机（SVM）和能够修改培训数据和标签的攻击者的学习者的冲突目标。我们开发了一种完全分布的迭代算法，以捕获每个节点对对抗行为的实时反应。数值结果表明，分布式SVM容易在不同类型的攻击中失败，并且它们的影响对网络结构和攻击功能具有很大的依赖。
Distributed machine learning algorithms play a significant role in processing massive data sets over large networks. However, the increasing reliance on machine learning on information and communication technologies (ICTs) makes it inherently vulnerable to cyber threats. This work aims to develop secure distributed algorithms to protect the learning from data poisoning and network attacks. We establish a game-theoretic framework to capture the conflicting goals of a learner who uses distributed support vector machines (SVMs) and an attacker who is capable of modifying training data and labels. We develop a fully distributed and iterative algorithm to capture real-time reactions of the learner at each node to adversarial behaviors. The numerical results show that distributed SVM is prone to fail in different types of attacks, and their impact has a strong dependence on the network structure and attack capabilities.
