Results 51 to 60 of about 17,780 (281)

A Survey on Adversarial Attacks for Malware Analysis

IEEE Access
Machine learning-based malware analysis approaches are widely researched and deployed in critical infrastructures for detecting and classifying evasive and growing malware threats.
Kshitiz Aryal, Maanak Gupta, Mahmoud Abdelsalam, Pradip Kunwar, Bhavani Thuraisingham   +4 more
doaj   +1 more source

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
doaj   +1 more source

Advancing Lithium–Oxygen Batteries: Pioneering Cathode Catalyst Innovation and Artificial Intelligence‐Driven Design Paradigms

Advanced Materials, EarlyView.
This review summarizes the principles and challenges of nonaqueous lithium‐oxygen batteries and recent advances in cathode catalysts, including carbon‐based materials, metals, oxides, sulfides, nitrides, carbides, and redox mediators. It highlights emerging design strategies and artificial intelligence‐driven approaches, emphasizing data‐assisted ...
Yuqing Yao, Chongyang Hao, Karol Viviana Mejia‐Centeno, Malik Dilshad Khan, Shang Wang, Longqiu Li, Yanhong Tian, Andreu Cabot, Qing Sun   +8 more
wiley   +1 more source

Deep Architecture Enhancing Robustness to Noise, Adversarial Attacks, and Cross-Corpus Setting for Speech Emotion Recognition [PDF]

, 2020
Speech emotion recognition systems (SER) can achieve high accuracy when the training and test data are identically distributed, but this assumption is frequently violated in practice and the performance of SER systems plummet against unforeseen data ...
Raja Jurdak, Björn W. Schuller, Siddique Latif, Latif, Siddique, Rana, Rajib, Sara Khalifa, Schuller, Björn W., Khalifa, Sara, Jurdak, Raja, Rajib Rana   +9 more
core   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Adversarial Ranking Attack and Defense [PDF]

, 2020
Deep Neural Network (DNN) classifiers are vulnerable to adversarial attack, where an imperceptible perturbation could result in misclassification. However, the vulnerability of DNN-based image ranking systems remains under-explored. In this paper, we propose two attacks against deep ranking systems, i.e., Candidate Attack and Query Attack, that can ...
Mo Zhou, Zhenxing Niu, Le Wang 0003, Qilin Zhang 0004, Gang Hua 0001   +4 more
openaire   +2 more sources

Robust ConvLSTM Model With Deep Reinforcement Learning for Stealth Attack Detection in Smart Grids

IEEE Open Journal of the Industrial Electronics Society
The advent of modern electricity distribution systems, comprising digital communication technologies and principles, has triggered a new era of smart grids, in which advanced metering infrastructure plays a crucial role in functions, such as digital ...
Ahmad N. Alkuwari, Abdullatif Albaseer, Saif Al-Kuwari, Marwa Qaraqe   +3 more
doaj   +1 more source

Universal Adversarial Training Using Auxiliary Conditional Generative Model-Based Adversarial Attack Generation

Applied Sciences, 2023
While Machine Learning has become the holy grail of modern-day computing, it has many security flaws that have yet to be addressed and resolved. Adversarial attacks are one of these security flaws, in which an attacker appends noise to data samples that ...
Hiskias Dingeto, Juntae Kim
doaj   +1 more source

Defending Poisoning Attacks in Federated Learning via Adversarial Training Method

, 2020
Recently, federated learning has shown its significant advantages in protecting training data privacy by maintaining a joint model across multiple clients.
Chen, Bing, Di Wu, Liu, Chengyong, Chengyong Liu, Zhang, Jiale, Jiale Zhang, Bing Chen, Wu, Di   +7 more
core   +1 more source

Hidden Conditional Adversarial Attacks

, 2022
Deep neural networks are vulnerable to maliciously crafted inputs called adversarial examples. Research on unprecedented adversarial attacks is significant since it can help strengthen the reliability of neural networks by alarming potential threats ...
Byun, JunYoung, Kim, Changick, Shim, KyuJin, GO, HYOJUN   +3 more
core   +1 more source