Results 51 to 60 of about 85,609 (269)

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

A Robust Method to Protect Text Classification Models against Adversarial Attacks

Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022
Text classification is one of the main tasks in natural language processing. Recently, adversarial attacks have shown a substantial negative impact on neural network-based text classification models. There are few defenses to strengthen model predictions
BALA MALLIKARJUNARAO GARLAPATI, Ajeet Kumar Singh, Srinivasa Rao Chalamala   +2 more
doaj   +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, Gang Hua 0001   +4 more
openaire   +2 more sources

Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare

Advanced Science, EarlyView.
Multimodal biosensing meets multidomain AI. Wearable biosensors capture complementary biochemical and physiological signals, while cross‐device, population‐aware learning aligns noisy, heterogeneous streams. This Review distills key sensing modalities, fusion and calibration strategies, and privacy‐preserving deployment pathways that transform ...
Chenshu Liu, Haolin Fan, Minwoo Kim, Tong Zhou, Pinyi Yang, Lingdi Zhao, Yiran Wang, Ziyuan Che, Chia‐Wei Liu, Bingbing Li, Yangzhi Zhu   +10 more
wiley   +1 more source

Multi-Stage Adversarial Defense for Online DDoS Attack Detection System in IoT

IEEE Access
Machine 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, Surafel Lemma Abebe, Henock Mulugeta Melaku   +2 more
doaj   +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

Breaking and Healing: GAN-Based Adversarial Attacks and Post-Adversarial Recovery for 5G IDSs

IEEE Access
Generative adversarial networks (GANs) have advanced rapidly in data augmentation and generation, and researchers have been exploring their applications in other areas, including adversarial attack generation.
Yasmeen Alslman, Mouhammd Alkasassbeh, Mohammad J. Abdel-Rahman   +2 more
doaj   +1 more source

Adversarial attacks for mixtures of classifiers

CoRR, 2023
Mixtures of classifiers (a.k.a. randomized ensembles) have been proposed as a way to improve robustness against adversarial attacks. However, it has been shown that existing attacks are not well suited for this kind of classifiers. In this paper, we discuss the problem of attacking a mixture in a principled way and introduce two desirable properties of
Lucas Gnecco Heredia, Benjamin Négrevergne, Yann Chevaleyre   +2 more
openaire   +2 more sources

Artificial Intelligence for Bone: Theory, Methods, and Applications

Advanced Intelligent Discovery, EarlyView.
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen   +3 more
wiley   +1 more source

Adversarial Robustness of Deep Reinforcement Learning Based Dynamic Recommender Systems

Frontiers in Big Data, 2022
Adversarial attacks, e.g., adversarial perturbations of the input and adversarial samples, pose significant challenges to machine learning and deep learning techniques, including interactive recommendation systems.
Siyu Wang, Yuanjiang Cao, Xiaocong Chen, Lina Yao, Xianzhi Wang, Quan Z. Sheng   +5 more
doaj   +1 more source