Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review
ML Mohus, J Li - arXiv preprint arXiv:2306.00687, 2023 - arxiv.org
As the adoption of machine learning models increases, ensuring robust models against
adversarial attacks is increasingly important. With unsupervised machine learning gaining …
adversarial attacks is increasingly important. With unsupervised machine learning gaining …
Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems
Machine Learning (ML) has been instrumental in enabling joint transceiver optimization by
merging all physical layer blocks of the end-to-end wireless communication systems …
merging all physical layer blocks of the end-to-end wireless communication systems …
Exploiting the Divergence Between Output of ML Models to Detect Adversarial Attacks in Streaming IoT Applications
A Albaseer, M Abdallah… - ICC 2023-IEEE …, 2023 - ieeexplore.ieee.org
The majority of streaming Internet of Things (IoT) applications use machine learning models
to identify and classify streaming inputs before forwarding them for further processing. These …
to identify and classify streaming inputs before forwarding them for further processing. These …
AaN: Anti-adversarial Noise-A Novel Approach for Securing Machine Learning-based Wireless Communication Systems
Machine Learning (ML) is becoming a cornerstone enabling technology for the next
generation of wireless systems. This is mainly due to the high performance achieved by …
generation of wireless systems. This is mainly due to the high performance achieved by …