Results 221 to 230 of about 79,918 (254)

Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey

IEEE Communications Surveys and Tutorials, 2023
Ke He, Dong Seong Kim, Muhammad Rizwan Asghar   +2 more
exaly  

Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey

IEEE Communications Surveys and Tutorials, 2023
Yulong Wang, Xin Yuan, Wei Ni
exaly  

Adversarial Examples and Defenses

2022
Maung Maung April Pyone, Yuma Kinoshita, Hitoshi Kiya   +2 more
openaire   +1 more source

A Survey of Adversarial Attack and Defense Methods for Malware Classification in Cyber Security

IEEE Communications Surveys and Tutorials, 2023
Senming Yan, Jing Ren, Wei Wang
exaly  

A Survey on Generative Adversarial Networks: Variants, Applications, and Training

ACM Computing Surveys, 2022
Songyuan Li
exaly  

Generative Adversarial Networks

ACM Computing Surveys, 2022
Zhipeng Cai, Honghui Xu, Yi Pan
exaly  

Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

ACM Computing Surveys, 2022
Ishai Rosenberg, Asaf Shabtai
exaly  

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
Jinxin Liu, Michele Nogueira, Burak Kantarci   +2 more
exaly  

Defense Mechanisms Against Adversarial Attacks

Adversarial attacks are particularly cybersecurity applications where reliability and accuracy are the most important. They are a significant threat to artificial intelligence systems (AI). These attacks involve subtle manipulation of input data developed into deceptive AI models that lead to false output or system dusk.
openaire   +1 more source

Adversarial Machine Learning in Wireless Communications Using RF Data: A Review

IEEE Communications Surveys and Tutorials, 2023
Damilola Adesina, Yalin E Sagduyu, Lijun Qian   +2 more
exaly