【摘要】 机器学习以强大的自适应性和自学习能力成为网络空间防御的研究热点和重要方向.然而机器学习模型在网络空间环境下存在受到对抗攻击的潜在风险,可能成为防御体系中最为薄弱的环节,从而危害整个系统的安全.为此科学分析安全问题场景,从运行机理上探索算法可行性和安全性,对运用机器学习模型构建网络空间防御系统大有裨益.全面综述对抗机器学习这一跨学科研究领域在网络空间防御中取得的成果及以后的发展方向.首先,介绍了网络空间防御和对抗机器学习等背景知识;其次,针对机器学习在网络空间防御中可能遭受的攻击,引入机器学习敌手模型概念,目的是科学评估其在特定威胁场景下的安全属性;然后,针对网络空间防御的机器学习算法,分别论述了在测试阶段发动规避攻击、在训练阶段发动投毒攻击、在机器学习全阶段发动隐私窃取的方法,进而研究如何在网络空间对抗环境下,强化机器学习模型的防御方法;最后,展望了网络空间防御中对抗机器学习研究的未来方向和有关挑战.更多还原

【Abstract】 Machine learning has the ability to learn in various conditions, and becomes a research hotspot and an important direction for cyberspace defense. Unfortunately, machine learning models have potential risks of suffering adversarial attacks in the cyberspace and may become the weakest part of the defense system. Therefore, it is of great benefit to discuss cyberspace defense scenarios and the fundamental issues about the possibility and security of using machine learning algorithms, which is the basis of building cyberspace defense system with machine learning models later on. Adversarial machine learning for cyberspace defense is an interdisciplinary research field. In this paper, we provide a comprehensive review of works related to this filed. Firstly, we present the background and related works of cyberspace defense and adversarial machine learning. Secondly, we provide a model to describe the adversarial model of attack against machine learning in cyberspace defense systems, and thoroughly assess its security attributes under specific threat scenarios. Specifically, we discuss the methods of launching evasion attacks in the test phase, launching poisoning attacks in the training phase, and launching privacy violation in the whole phase for cyberspace defense systems. On the basis of this, we study how to strengthen the machine learning models with different defense mechanisms in cyberspace. Finally, we discuss the future works and challenges of research on adversarial machine learning in cyberspace defense.更多还原