| 李欣姣,吴国伟,姚琳,张伟哲,张宾.机器学习安全攻击与防御机制研究进展和未来挑战.软件学报,2021,32(2):406-423 |
| 机器学习安全攻击与防御机制研究进展和未来挑战 |
| Progress and Future Challenges of Security Attacks and Defense Mechanisms in Machine Learning |
| 投稿时间：2019-08-12 修订日期：2019-12-01 |
| DOI：10.13328/j.cnki.jos.006147 |
| 中文关键词: 机器学习 安全和隐私 攻击分类 防御机制 |
| 英文关键词:machine learning security and privacy attack classification defense mechanism |
| 基金项目:国家自然科学基金（61872053）；中央高校基本科研业务费专项资金（DUT19GJ204）；广东省重点领域研发计划（2019B010136001）；广东省重点科技计划（LZC0023） |
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| 中文摘要: |
| 机器学习的应用遍及人工智能的各个领域，但因存储和传输安全问题以及机器学习算法本身的缺陷，机器学习面临多种面向安全和隐私的攻击.基于攻击发生的位置和时序对机器学习中的安全和隐私攻击进行分类，分析和总结了数据投毒攻击、对抗样本攻击、数据窃取攻击和询问攻击等产生的原因和攻击方法，并介绍和分析了现有的安全防御机制.最后，展望了安全机器学习未来的研究挑战和方向. |
| 英文摘要: |
| Machine learning applications span all areas of artificial intelligence, but due to storage and transmission security issues and the flaws of machine learning algorithms themselves, machine learning faces a variety of security- and privacy-oriented attacks. This survey classifies the security and privacy attacks based on the location and timing of attacks in machine learning, and analyzes the causes and attack methods of data poisoning attacks, adversary attacks, data stealing attacks, and querying attacks. Furthermore, the existing security defense mechanisms are summarized. Finally, a perspective of future work and challenges in this research area are discussed. |
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