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MTD-AD: Moving Target Defense as Adversarial Defense
IEEE Transactions on Dependable and Secure ComputingNetwork Intrusion Detection Systems (NIDSes) are increasingly incorporating Machine Learning (ML) and Deep Learning (DL) algorithms for detecting network intrusions. However, ML/DL algorithms are susceptible to adversarial examples, which can lead to the misclassification of input data.
Ke He +2 more
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Variational Adversarial Defense: A Bayes Perspective for Adversarial Training
IEEE Transactions on Pattern Analysis and Machine IntelligenceVarious methods have been proposed to defend against adversarial attacks. However, there is a lack of enough theoretical guarantee of the performance, thus leading to two problems: First, deficiency of necessary adversarial training samples might attenuate the normal gradient's back-propagation, which leads to overfitting and gradient masking ...
Chenglong Zhao +5 more
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Adversarial Defense in Aerial Detection
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023Yuwei Chen, Shiyong Chu
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Attack-less adversarial training for a robust adversarial defense
Applied Intelligence, 2021Jiacang Ho, Byung-Gook Lee, Dae-Ki Kang
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A Survey of Adversarial Attack and Defense Methods for Malware Classification in Cyber Security
IEEE Communications Surveys and Tutorials, 2023Quan Yu
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A Survey of Adversarial Defenses and Robustness in NLP
ACM Computing Surveys, 2023Balaraman Ravindran, Mitesh M Khapra
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Adversarial attacks and defenses in deep learning for image recognition: A survey
Neurocomputing, 2022Jianqiang Li, Chengwen Luo, Qiuzhen Lin
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Adversarial attack and defense technologies in natural language processing: A survey
Neurocomputing, 2022Wen Huang, Shijie Zhou, Shilin Qiu
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