JAMC
Research Trends in Adversarial Machine Learning
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Abstract
The field of machine learning artificial intelligence algorithms has made significant progress. With the development of the times, the security brought by machine learning is worthy of consideration, and adversarial machine learning improves the reliability of machine learning algorithms through continuous training algorithms. Hence, the research of adversarial machine learning based on network security is well worth exploring.
References
[1] J. Yang. Research on some problems in adversarial machine learning [D]. Hainan University, 2021.
[2] Szegedy C, Zaremba W, Sutskever I, et al. Intriguing properties of neural networks, 2013.
[3] Liu QX, Wang JunN, Yin J, et al. application of adversarial machine learning in network intrusion detection [J]. Journal of Communication, 2021, 042(011):1-12.
[4] Jiang Yan, Zhang Liguo. A review of adversarial attack and defense methods for deep learning models [J]. Computer Engineering, 2021, 47(1):11.
[5] Xiaoyong Yuan, Pan He, Qile Zhu, et al. Adversarial Examples: Attacks and Defenses for Deep Learning, 2017.
How to cite this paper
Research Trends in Adversarial Machine Learning
How to cite this paper: Jihang Jiang. (2022) Research Trends in Adversarial Machine Learning. Journal of Applied Mathematics and Computation, 6(4), 535-539.
DOI: http://dx.doi.org/10.26855/jamc.2022.12.018
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