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AI-Driven Innovations in Forensic Odontology: Challenges and Opportunities.

J Pharm Bioallied Sci
Sinha S, Srivastava T, Rehan AD, Pandey S.   +3 more
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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
semanticscholar   +1 more source

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, M. 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

Model Evasion Attack on Intrusion Detection Systems using Adversarial Machine Learning

Annual Conference on Information Sciences and Systems, 2020
Intrusion Detection Systems (IDS) have a long history as an effective network defensive mechanism. The systems alert defenders of suspicious and / or malicious behavior detected on the network.
Md. Ahsan Ayub, William A. Johnson, Douglas A. Talbert, Ambareen Siraj   +3 more
semanticscholar   +1 more source

Adversarial Machine Learning in Cybersecurity: Attacks and Defenses

International Journal of Management Science Research
Adversarial Machine Learning (AML) refers to the research field that involves testing and improving machine learning models by introducing adversarial samples or attack techniques.
Hu Ke, Jian Xu, Yong Wang, Heyao Chen, Zepeng Shen   +4 more
semanticscholar   +1 more source