Deep Reinforcement Learning-Based Adversarial Attack and Defense in Industrial Control Systems
MathematicsAdversarial attacks targeting industrial control systems, such as the Maroochy wastewater system attack and the Stuxnet worm attack, have caused significant damage to related facilities.
Mun-Suk Kim
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Practical Attacks Against Graph-based Clustering
, 2017 Graph modeling allows numerous security problems to be tackled in a general way, however, little work has been done to understand their ability to withstand adversarial attacks.
Bayer Ulrich +19 more
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Advanced Materials, EarlyView.This review summarizes the latest progress in transforming renewable bamboo resources into high‐value functional materials. It emphasizes how to leverage bamboo's multiscale hierarchical structures to realize innovative applications in energy, construction, environment, and medicine, providing key insights for developing high‐performance bio‐based ...
Yuxiang Huang +13 more
wiley +1 more source
Dual-Targeted Textfooler Attack on Text Classification Systems
IEEE Access, 2023 Deep neural networks provide good performance on classification tasks such as those for image, audio, and text classification. However, such neural networks are vulnerable to adversarial examples.
Hyun Kwon
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Balancing fairness to victims, society and defendants in the cross-examination of vulnerable witnesses: an impossible triangulation? [PDF]
, 2014 This article argues that direct cross-examination of vulnerable witnesses should be removed from Australian trials, to reduce any illegitimate advantage to the defendant.
David Plater +2 more
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Attentive Adversarial Learning for Domain-Invariant Training
, 2019 Adversarial domain-invariant training (ADIT) proves to be effective in suppressing the effects of domain variability in acoustic modeling and has led to improved performance in automatic speech recognition (ASR).
Gong, Yifan, Li, Jinyu, Meng, Zhong
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Learning Crystallographic Disorder: Bridging Prediction and Experiment in Materials Discovery
Advanced Materials, EarlyView.Machine learning based computational materials discovery workflows have recently proposed thousands of potentially stable crystalline materials. However, the experimental realization of these predictions is often challenging because the models assume perfectly ordered structures.
Konstantin S. Jakob +3 more
wiley +1 more source
Extended Spatially Localized Perturbation GAN (eSLP-GAN) for Robust Adversarial Camouflage Patches
Sensors, 2021 Deep neural networks (DNNs), especially those used in computer vision, are highly vulnerable to adversarial attacks, such as adversarial perturbations and adversarial patches. Adversarial patches, often considered more appropriate for a real-world attack,
Yongsu Kim +5 more
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Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains
, 2017 While modern day web applications aim to create impact at the civilization level, they have become vulnerable to adversarial activity, where the next cyber-attack can take any shape and can originate from anywhere. The increasing scale and sophistication
Kantardzic, Mehmed, Sethi, Tegjyot Singh
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Active Learning‐Guided Accelerated Discovery of Ultra‐Efficient High‐Entropy Thermoelectrics
Advanced Materials, EarlyView.An active learning framework is introduced for the accelerated discovery of high‐entropy chalcogenides with superior thermoelectric performance. Only 80 targeted syntheses, selected from 16206 possible combinations, led to three high‐performance compositions, demonstrating the remarkable efficiency of data‐driven guidance in experimental materials ...
Hanhwi Jang +8 more
wiley +1 more source