A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity
IEEE Access, 2020 Machine learning algorithms are widely utilized in cybersecurity. However, recent studies show that machine learning algorithms are vulnerable to adversarial examples.
Sicong Zhang, Xiaoyao Xie, Yang Xu
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Applied Sciences, 2020 State-of-the-art neural network models are actively used in various fields, but it is well-known that they are vulnerable to adversarial example attacks.
Sanglee Park, Jungmin So
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Survey of Adversarial Attacks and Defense Methods for Deep Learning Model [PDF]
Jisuanji gongcheng, 2021 As an important part of artificial intelligence technology,deep learning is widely used in computer vision,natural language processing and other fields.Although deep learning performs well in tasks such as image classification and target detection,its ...
JIANG Yan, ZHANG Liguo
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Adversarial Attack for SAR Target Recognition Based on UNet-Generative Adversarial Network
Remote Sensing, 2021 Some recent articles have revealed that synthetic aperture radar automatic target recognition (SAR-ATR) models based on deep learning are vulnerable to the attacks of adversarial examples and cause security problems.
Chuan Du, Lei Zhang
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Secure machine learning against adversarial samples at test time
EURASIP Journal on Information Security, 2022 Deep neural networks (DNNs) are widely used to handle many difficult tasks, such as image classification and malware detection, and achieve outstanding performance.
Jing Lin, Laurent L. Njilla, Kaiqi Xiong
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Adversarial Attack Transferability Enhancement Algorithm Based on Input Channel Splitting [PDF]
Jisuanji gongcheng, 2023 The Deep Neural Network(DNN) has been widely used in face recognition, automatic driving, and other scenarios;however, it is vulnerable to attacks by adversarial samples.Methods by which adversarial samples are generated can be classified into white-box ...
ZHENG Desheng, CHEN Jixin, ZHOU Jing, KE Wuping, LU Chao, ZHOU Yong, QIU Qian
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Adversarial attacks against supervised machine learning based network intrusion detection systems.
PLoS ONE, 2022 Adversarial machine learning is a recent area of study that explores both adversarial attack strategy and detection systems of adversarial attacks, which are inputs specially crafted to outwit the classification of detection systems or disrupt the ...
Ebtihaj Alshahrani +3 more
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Using Frequency Attention to Make Adversarial Patch Powerful Against Person Detector
IEEE Access, 2023 Deep neural networks (DNNs) are vulnerable to adversarial attacks. In particular, object detectors may be attacked by applying a particular adversarial patch to the image.
Xiaochun Lei +5 more
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Multi-Targeted Adversarial Example in Evasion Attack on Deep Neural Network
IEEE Access, 2018 Deep neural networks (DNNs) are widely used for image recognition, speech recognition, pattern analysis, and intrusion detection. Recently, the adversarial example attack, in which the input data are only slightly modified, although not an issue for ...
Hyun Kwon +4 more
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Multi-Stage Adversarial Defense for Online DDoS Attack Detection System in IoT
IEEE AccessMachine learning-based Distributed Denial of Service (DDoS) attack detection systems have proven effective in detecting and preventing DDoD attacks in Internet of Things (IoT) systems.
Yonas Kibret Beshah +2 more
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