Results 21 to 30 of about 5,384,791 (287)
Remote SensingSynthetic Aperture Radar (SAR) target detection models are highly vulnerable to adversarial attacks, which significantly reduce detection performance and robustness.
Jiahao Cui +4 more
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Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT [PDF]
, 2021 As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought.
William J. Buchanan +13 more
core +1 more source
Adversarial Example Remaining Availability and Functionality [PDF]
Jisuanji kexue yu tansuo, 2022 Malware detection method based on gray images has received a lot of attention because it does not require disassembly and can obtain a high detection accuracy.
XIAO Mao, GUO Chun, SHEN Guowei, JIANG Chaohui
doaj +1 more source
Exploring Adversarial Examples in Malware Detection [PDF]
2019 IEEE Security and Privacy Workshops (SPW), 2019 The convolutional neural network (CNN) architecture is increasingly being applied to new domains, such as malware detection, where it is able to learn malicious behavior from raw bytes extracted from executables. These architectures reach impressive performance with no feature engineering effort involved, but their robustness against active attackers ...
Octavian Suciu +2 more
openaire +4 more sources
Self-Supervised Adversarial Example Detection by Disentangled Representation
, 2022 Deep learning models are known to be vulnerable to adversarial examples that are elaborately designed for malicious purposes and are imperceptible to the human perceptual system. Autoencoder, when trained solely over benign examples, has been widely used
Zheng, Xufei +4 more
core +2 more sources
Exploring Diverse Feature Extractions for Adversarial Audio Detection
IEEE Access, 2023 Although deep learning models have exhibited excellent performance in various domains, recent studies have discovered that they are highly vulnerable to adversarial attacks.
Yujin Choi +3 more
doaj +1 more source
WordRevert: Adversarial Examples Defence Method for Chinese Text Classification
IEEE Access, 2022 Adversarial examples can evade the detection of text classification models based on Deep Neural Networks (DNNs), thus posing a potential security threat to the system.
Enhui Xu +5 more
doaj +1 more source
Journal of Imaging, 2022 Automatic Speech Recognition (ASR) systems are ubiquitous in various commercial applications. These systems typically rely on machine learning techniques for transcribing voice commands into text for further processing.
Wei Zong +4 more
doaj +1 more source
Detecting chaos in adversarial examples
Chaos, Solitons & Fractals, 2022 The puzzling phenomenon of adversarial examples continues to attract significant research within the machine learning community. The confirmation that adversarial examples can arise in natural real-life circumstances has but increased the interest. While several methods have been proposed for both generating adversarial examples and defending against ...
Oscar Deniz +2 more
openaire +1 more source
A Cascade Defense Method for Multidomain Adversarial Attacks under Remote Sensing Detection
Remote Sensing, 2022 Deep neural networks have been widely used in detection tasks based on optical remote sensing images. However, in recent studies, deep neural networks have been shown to be vulnerable to adversarial examples.
Wei Xue +4 more
doaj +1 more source