Progress and Future Challenges of Security Attacks and Defense Mechanisms in Machine Learning

doi:10.13328/j.cnki.jos.006147

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2025-4-24- 20

Home > Archive>Volume 32, Issue 2, 2021 >406-423. DOI:10.13328/j.cnki.jos.006147

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Progress and Future Challenges of Security Attacks and Defense Mechanisms in Machine Learning
DOI:
                        10.13328/j.cnki.jos.006147
                    
Author:
                        LI Xin-JiaoLI Xin-Jiao
School of Software Technology, Dalian University of Technology, Dalian 116620, China;Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province(Dalian University of Technology), Dalian 116620, China
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WU Guo-WeiWU Guo-Wei
School of Software Technology, Dalian University of Technology, Dalian 116620, China;Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province(Dalian University of Technology), Dalian 116620, China
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YAO LinYAO Lin
School of Software Technology, Dalian University of Technology, Dalian 116620, China;Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen 518055, China
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ZHANG Wei-ZheZHANG Wei-Zhe
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
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ZHANG BinZHANG Bin
Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen 518055, China
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Fund Project: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.

Key words:machine learning;security and privacy;attack classification;defense mechanism

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

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