Progress and Future Challenges of Security Attacks and Defense Mechanisms in Machine Learning
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National Natural Science Foundation of China (61872053); Fundamental Research Funds for the Central Universities (DUT19GJ204); Key-Area Research and Development Program of Guangdong Province (2019B010136001); Key Science and Technology Program of Guangdong Province (LZC0023)

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    Abstract:

    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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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

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History
  • Received:August 12,2019
  • Revised:December 01,2019
  • Adopted:
  • Online: October 12,2020
  • Published: February 06,2021
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