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Adversarial Attacks and Defenses
ACM SIGKDD Explorations Newsletter, 2021 Despite the recent advances in a wide spectrum of applications, machine learning models, especially deep neural networks, have been shown to be vulnerable to adversarial attacks. Attackers add carefully-crafted perturbations to input, where the perturbations are almost imperceptible to humans, but can cause models to make wrong predictions.
Liu, Ninghao +4 more
openaire +2 more sources
A Survey on Adversarial Attacks for Malware Analysis
IEEE AccessMachine learning-based malware analysis approaches are widely researched and deployed in critical infrastructures for detecting and classifying evasive and growing malware threats.
Kshitiz Aryal +4 more
doaj +1 more source
Adversarial Feature Selection Against Evasion Attacks [PDF]
IEEE Transactions on Cybernetics, 2016 Pattern recognition and machine learning techniques have been increasingly adopted in adversarial settings such as spam, intrusion and malware detection, although their security against well-crafted attacks that aim to evade detection by manipulating data at test time has not yet been thoroughly assessed.
Zhang F +4 more
openaire +4 more sources
Computational Modeling Meets 3D Bioprinting: Emerging Synergies in Cardiovascular Disease Modeling
Advanced Healthcare Materials, EarlyView.Emerging advances in three‐dimensional bioprinting and computational modeling are reshaping cardiovascular (CV) research by enabling more realistic, patient‐specific tissue platforms. This review surveys cutting‐edge approaches that merge biomimetic CV constructs with computational simulations to overcome the limitations of traditional models, improve ...
Tanmay Mukherjee +7 more
wiley +1 more source
All‐Optical Reconfigurable Physical Unclonable Function for Sustainable Security
Advanced Materials, EarlyView.An all‐optical reconfigurable physical unclonable function (PUF) is demonstrated using plasmonic coupling–induced sintering of optically trapped gold nanoparticles, where Brownian motion serves as a robust entropy source. The resulting optical PUF exhibits high encoding density, strong resistance to modeling attacks, and practical authentication ...
Jang‐Kyun Kwak +4 more
wiley +1 more source
Robust ConvLSTM Model With Deep Reinforcement Learning for Stealth Attack Detection in Smart Grids
IEEE Open Journal of the Industrial Electronics SocietyThe 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 +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 +2 more
doaj +1 more source
Advanced Science, EarlyView.This study generates high‐fidelity synthetic longitudinal records for a million‐patient diabetes cohort, successfully replicating clinical predictive performance. However, deeper analysis reveals algorithmic biases and trajectory inconsistencies that escape standard quality metrics. These findings challenge current validation norms, demonstrating why a
Francisco Ortuño +5 more
wiley +1 more source
Multi-Stage Adversarial Defense for Online DDoS Attack Detection System in IoT
IEEE AccessMachine 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 +2 more
doaj +1 more source