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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.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.
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图 2 网络空间防御中的对抗攻击与防御措施
Fig. 2 Adversarial attack and defense methods for cyberspace defense
图 4 原始PDF 文件(左图)和修改后的PDF文件(右图)
Fig. 4 The original (left) and modified (right) PDF file
图 6 基于强化学习的恶意软件规避框架
Fig. 6 Framework of malware evasion based on reinforcement learning
图 7 针对质心异常检测的投毒攻击
Fig. 7 The illustration of poisoning attack for centroid anomaly detection
表 1 对抗机器学习相关综述
Table 1 Related surveys about adversarial machine learning
类别 文献题目 主要内容 发表年份 机器学习模型 SoK: Security and privacy in machine learning[15] 分析机器学习模型的攻击面, 系统论述机器学习模型在训练和推断过程中可能遭受的攻击以及防御措施. 2018 Wild patterns: Ten years after the rise of adversarial machine learning[7] 系统揭示对抗机器学习演进路线, 内容涵盖计算机视觉以及网络安全等领域 2018 A survey on security threats and defensive techniques of machine learning: A data driven view[12] 从数据驱动视角论述机器学习的对抗攻击和防御问题. 2018 The security of machine learning in an adversarial setting: A survey[13] 论述对抗环境下, 机器学习在训练和推断/测试阶段遭受的攻击, 提出相应的安全评估机制和对应的防御策略 2019 A taxonomy and survey of attacks against machine learning[14] 论述机器学习应用于不同领域时的对抗攻击, 主要包括入侵检测、垃圾邮件过滤、视觉检测等领域. 2019 机器学习模型安全与隐私研究综述[16] 从数据安全、模型安全以及模型隐私三个角度对现有的攻击和防御研究进行系统总结和归纳 2021 机器学习安全攻击与防御机制研究进展和未来挑战[11] 基于攻击发生的位置和时序对机器学习安全和隐私攻击进行分类, 并对现有攻击方法和安全防御机制进行介绍 2021 深度学
习模型Survey of attacks and defenses on edge-deployed neural networks[18] 论述边缘神经网络的攻击与防御 2019 Adversarial examples in modern machine learning: A review[19] 论述对抗样本生成与防御技术 2019 A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and Interpretability[20] 论述深度神经网络(Deep neural network, DNN)的安全与可解释性 2020 对抗样本生成技术综述[21] 围绕前传、起源和发展三个阶段对对抗样本进行综述 2020 机器学习隐私 机器学习的隐私保护研究综述[17] 着重论述机器学习的隐私保护技术 2020 A survey of privacy attacks in machine learning[22] 论述机器学习中隐私攻击与保护技术 2020 机器学习隐私保护研究综述[23] 着重论述机器学习的隐私保护技术 2020 计算机视觉 Threat of adversarial attacks on deep learning in computer vision: A survey[24] 论述计算机视觉中深度学习模型的攻击与防御 2018 Adversarial machine learning in image classification: A survey towards the defender＇s perspective[25] 从防御角度研究计算机视觉分类问题中的对抗机器学习 2020 Adversarial examples on object recognition: A comprehensive survey[26] 论述神经网络在视觉领域应用时, 存在的对抗样本的攻防问题 2020 Adversarial attacks on deep learning models of computer vision: A survey[27] 论述计算机视觉中深度学习模型的对抗攻击 2020 自然语言处理 Adversarial attacks on deep-learning models in natural language processing[28] 论述自然语言处理领域中深度学习模型的对抗攻击与防御问题 2020 生物医疗领域 Adversarial biometric recognition: A review on biometric system security from the adversarial machine-learning perspective[29] 首次从对抗机器学习角度论述生物识别系统的安全问题 2015 Toward an understanding of adversarial examples in clinical trials[30] 论述基于深度学习模型的临床实验中的对抗样本问题 2018 Secure and robust machine learning for healthcare: A Survey[31] 从对抗机器学习的角度概述医疗保健领域中机器学习应用的现状、挑战及解决措施 2021 网络空间防御 Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues[32] 论述入侵检测系统中的对抗攻击问题以及应对措施 2013 Towards adversarial malware detection: Lessons learned from PDF-based attacks[33] 论述基于机器学习的恶意便携式文档格式 (Portable document format, PDF)文件检测系统可能遭受的对抗攻击 2019 
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表 2 对抗机器学习时间线
Table 2 A timeline of adversarial machine learning history
年份 主要内容 2004 Dalvi 等[42] 和 Lowd 等[43-44] 研究了垃圾邮件检测中的对抗问题, 提出线性分类模型可能被精心设计的对抗样本所愚弄 2006 Barreno 等[8] 从更广泛的角度质疑机器学习模型在对抗环境中的适用性问题, 并提出一些可行措施来消除或降低这些威胁 2007 NeurIPS 举办 Machine Learning in Adversarial Environments for Computer Security研讨会. 2010年, Machine Learning期刊为该研讨会设立同名专题[54] 2008 CCS 举办首届人工智能与安全研讨会 AISec(Workshop on Artificial Intelligence and Security), 并且持续举办至2020年 2012 面向计算机安全的机器学习方法达堡展望研讨会 (Dagstuhl Perspectives Workshop on Machine Learning Methods for Computer Security), 探讨对抗学习和基于学习的安全技术面临的挑战和未来研究方向[55] 2014 KDD 举办安全与隐私特别论坛 2016 AAAI 举办面向网络空间安全的人工智能研讨会 AICS (Artificial Intelligence for Cyber Security), 此后至2019年每年举办一届 2017 为促进对抗样本的相关研究, 谷歌大脑 (Google Brain) 在 NeurIPS2017 上举办对抗攻击与防御挑战赛 2018 NeurIPS2018举办对抗视觉挑战赛, 目的是促进更加鲁棒的机器视觉模型和更为广泛可用的对抗攻击 Yevgeniy 等[6]撰写书籍 Adversarial Machine Learning, 并由 Morgan & Claypool 出版社发行 2019 Joseph 等[5]撰写书籍 Adversarial Machine Learning, 并由剑桥大学出版社发行 论文Adversarial attacks on medical machine learning[56] Science 期刊上发表, 指出医疗机器学习中出现新脆弱性问题, 需要新
举措论文 Why deep-learning AIs are so easy to fool[57] 在 Nature期刊上发表, 探讨深度学习遭受对抗攻击时的鲁棒性 KDD2019 举办首届面向机器学习和数据挖掘的对抗学习方法研讨会, 至今已连续举办两届 清华大学和阿里安全于天池竞赛平台联合举办安全 AI 挑战者计划, 至今已有 5 期. 同时, 每年底举办 AI 与安全研讨会, 至今已连续举办两届. 2020 KDD2020 举办首届面向安全防御的可部署机器学习国际研讨会 (Workshop on Deployable Machine Learning for Security Defense) 2021 AAAI2021 举办鲁棒、安全、高效的机器学习国际研讨会 (Towards Robust, Secure and Efficient Machine Learning) 注: 数据更新至2021年2月8日.
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表 3 基于威胁建模的机器学习攻击分类
Table 3 Classfication of attacks against machine learning based on threat model
敌手能力 敌手目标 敌手知识 模型完整性 模型可用性 隐私窃取 测试数据 规避攻击 — 模型提取
模型反演
成员推断白盒攻击
黑盒攻击训练数据 投毒攻击(后门攻击) 投毒攻击(油蛙攻击) 模型反演
成员推断白盒攻击
黑盒攻击
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表 4 网络空间防御中的典型对抗攻击
Table 4 Typical adversarial attacks for cyberspace defense
攻击方法 相关文献 应用领域 特点 规避攻击 基于模仿的规避攻击 [42, 44, 64−66] 垃圾邮件检测 模仿攻击采用启发式算法, 尝试向恶意文件中添加良性特征或者向良性文件中注入恶意特征, 从而实现规避 [67] 流量分析 [68] 恶意软件检测 [62, 69−75] 恶意 PDF 文件分类 基于梯度的规避攻击 [75−77] 恶意 PDF 文件分类 基于梯度的规避攻击利用梯度下降求解优化问题, 对输入样本执行细粒度的修改, 以最小化 (最大化) 样本被归类为恶意 (良性) 的概率 [9, 78−79] 恶意软件检测 [63, 80] 入侵检测 基于迁移的规避攻击 [70, 81] 恶意 PDF 文件分类 基于迁移的规避攻击主要利用了对抗样本的跨模型迁移性, 可以应用于无法获取模型梯度的各种攻击场景 [82−84] 入侵检测 [85] XSS 检测 [86] 域名生成 [87−89] 恶意软件检测 投毒攻击 可用性攻击 [8, 44, 90−92] 垃圾邮件检测 可用性攻击的目的是增加测试阶段的分类误差, 从而造成拒绝服务 [93−94] 入侵检测 完整性攻击 [95−96] 异常检测 完整性攻击的目的是使得恶意软件特定子集被模型误分类 [97−98] 恶意软件检测 隐私窃取 模型提取攻击 [99] — 隐私窃取主要目的是窃取机器学习模型或训练数据的信息 模型反演攻击 [100−101] 成员推断攻击 [102−103]
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表 5 网络空间防御中用于对抗攻击的典型防御措施
Table 5 Typical defense against adversarial attacks for cyberspace defense
防御措施 相关文献 应用场景 简述 规避防御 数据降维 [117−118] 垃圾邮件检测 可以有效防御对抗攻击, 但模型对正常样本的精度可能降低 [118−119] 恶意软件检测 鲁棒优化 [120−124] 恶意软件检测 基本思想是模型在训练时存在“盲点”, 将构造的对抗样本注入训练集, 以提高模型的泛化能力 防御蒸馏 [125−129] 恶意软件检测 难以防御 C&W 攻击方法 投毒防御 数据清洗 [130] 异常检测 该方法将投毒攻击视为离群值进行处理 [131−136] — 博弈论 [137−141] 垃圾邮件检测 该方法将博弈论的思想用于处理垃圾邮件的投毒攻击 隐私保护 差分隐私 [142−149] — 该方法的难点在于如何平衡模型可用性与隐私保护效果 模型压缩 [109] 该方法可用于缓解成员推断攻击 模型集成 [150] 该方法的主要思想是将模型中低于特定阈值的损失梯度设为零, 可以用于防御模型提取攻击
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