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Adversarial Machine Learning: Methods for Attacks and Defenses

Van, Minh Hao
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Adversarial Machine Learning:

2023
This NIST AI report develops a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML). The taxonomy is built on survey of the AML literature and is arranged in a conceptual hierarchy that includes key types of ML methods and lifecycle stage of attack, attacker goals and objectives, and attacker capabilities and ...
Alina Oprea, Apostol Vassilev
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Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey

IEEE Communications Surveys and Tutorials, 2023
Network-based Intrusion Detection System (NIDS) forms the frontline defence against network attacks that compromise the security of the data, systems, and networks.
Ke He, Dan Dongseong Kim, M. R. Asghar
semanticscholar   +1 more source

Defenses in Adversarial Machine Learning: A Survey

arXiv.org, 2023
Adversarial phenomenon has been widely observed in machine learning (ML) systems, especially in those using deep neural networks, describing that ML systems may produce inconsistent and incomprehensible predictions with humans at some particular cases ...
Baoyuan Wu, Shaokui Wei, Mingli Zhu, Meixi Zheng, Zihao Zhu, Mingda Zhang, Hongrui Chen, Danni Yuan, Li Liu, Qingshan Liu   +9 more
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Adversarial Machine Learning: A Multilayer Review of the State-of-the-Art and Challenges for Wireless and Mobile Systems

IEEE Communications Surveys and Tutorials, 2022
Machine Learning (ML) models are susceptible to adversarial samples that appear as normal samples but have some imperceptible noise added to them with the intention of misleading a trained classifier and misclassifying the input.
Jinxin Liu, M. Nogueira, Johan Fernandes, B. Kantarci   +3 more
semanticscholar   +1 more source

Exploring Targeted and Stealthy False Data Injection Attacks via Adversarial Machine Learning

IEEE Internet of Things Journal, 2022
State estimation methods used in cyber–physical systems (CPSs), such as smart grid, are vulnerable to false data injection attacks (FDIAs). Although substantial deep learning methods have been proposed to detect such attacks, deep neural networks (DNNs ...
Jiwei Tian, Buhong Wang, Jing Li, Zhen Wang, Bowen Ma, Mete Ozay   +5 more
semanticscholar   +1 more source

Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning

ACM Computing Surveys
Multi-Agent Reinforcement Learning (MARL) is susceptible to Adversarial Machine Learning (AML) attacks. Execution-time AML attacks against MARL are complex due to effects that propagate across time and between agents.
Maxwell Standen, Junae Kim, Claudia Szabo   +2 more
semanticscholar   +1 more source

Recent advances in adversarial machine learning: status, challenges and perspectives

Defense + Commercial Sensing, 2021
The recent advances in machine learning (ML) and Artificial Intelligence (AI) have resulted in widespread application of data-driven learning algorithms. Rapid growth of AI/ML and their penetration within a plethora of civilian and military applications,
A. Rawal, D. Rawat, B. Sadler
semanticscholar   +1 more source

Adversarial Machine Learning for Image-Based Radio Frequency Fingerprinting: Attacks and Defenses

IEEE Communications Magazine
Image-based radio frequency fingerprinting (RFF) is a promising variant of traditional RFF systems. As a distinctive feature, such systems convert physical-layer signals into matrices resembling 2-D or 3-D images and consider the latter as the input for ...
Lorenzo Papangelo, Maurizio Pistilli, Savio Sciancalepore, G. Oligeri, G. Piro, G. Boggia   +5 more
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