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Quantum adversarial machine learning [PDF]
Physical Review Research, 2020 Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of machine learning approaches in adversarial settings and developing techniques accordingly to make learning robust to adversarial manipulations. It plays a vital
Sirui Lu, Lu-Ming Duan, Dong-Ling Deng
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Attack and Defense in Cellular Decision-Making: Lessons from Machine Learning
Physical Review X, 2019 Machine-learning algorithms can be fooled by small well-designed adversarial perturbations. This is reminiscent of cellular decision-making where ligands (called antagonists) prevent correct signaling, like in early immune recognition.
Thomas J. Rademaker +2 more
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Adversarial machine learning phases of matter
Quantum Frontiers, 2023 We study the robustness of machine learning approaches to adversarial perturbations, with a focus on supervised learning scenarios. We find that typical phase classifiers based on deep neural networks are extremely vulnerable to adversarial perturbations:
Si Jiang, Sirui Lu, Dong-Ling Deng
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STS-AT: A Structured Tensor Flow Adversarial Training Framework for Robust Intrusion Detection [PDF]
SensorsNetwork intrusion detection is a key technology for ensuring cybersecurity. However, current methods face two major challenges: reliance on manual feature engineering, which leads to the loss of discriminative information, and the vulnerability of deep ...
Juntong Zhu +4 more
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Adversarial Machine Learning [PDF]
, 2022 Recent innovations in machine learning enjoy a remarkable rate of adoption across a broad spectrum of applications, including cyber-security. While previous chapters study the application of machine learning solutions to cyber-security, in this chapter we present adversarial machine learning: a field of study concerned with the security of machine ...
Aneesh Sreevallabh Chivukula +4 more
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Adversarial Machine Learning [PDF]
IEEE Internet Computing, 2011 The author briefly introduces the emerging field of adversarial machine learning, in which opponents can cause traditional machine learning algorithms to behave poorly in security applications. He gives a high-level overview and mentions several types of attacks, as well as several types of defenses, and theoretical limits derived from a study of near ...
Anthony D. Joseph +3 more
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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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Adversarial Machine Learning - Industry Perspectives [PDF]
SSRN Electronic Journal, 2020 Minor Typos corrected 7 pages, 1 ...
Kumar, Ram Shankar Siva +7 more
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Adversarial attacks on deep learning models in smart grids
Energy Reports, 2022 A smart grid may employ various machine learning models for intelligent tasks, such as load forecasting, fault diagnosis and demand response. However, the research on adversarial machine learning has attracted broad interest recently with the rapid ...
Jingbo Hao, Yang Tao
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Sensors, 2023 Deep learning models have been used in creating various effective image classification applications. However, they are vulnerable to adversarial attacks that seek to misguide the models into predicting incorrect classes.
Mohammed Alkhowaiter +4 more
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