Security and privacy for 6G: A survey on prospective technologies and challenges
Sixth-generation (6G) mobile networks will have to cope with diverse threats on a space-air-
ground integrated network environment, novel technologies, and an accessible user …
ground integrated network environment, novel technologies, and an accessible user …
Recent advances in adversarial training for adversarial robustness
Adversarial training is one of the most effective approaches defending against adversarial
examples for deep learning models. Unlike other defense strategies, adversarial training …
examples for deep learning models. Unlike other defense strategies, adversarial training …
Adversarial examples: A survey of attacks and defenses in deep learning-enabled cybersecurity systems
Over the last few years, the adoption of machine learning in a wide range of domains has
been remarkable. Deep learning, in particular, has been extensively used to drive …
been remarkable. Deep learning, in particular, has been extensively used to drive …
Machine learning and blockchain technologies for cybersecurity in connected vehicles
Future connected and autonomous vehicles (CAVs) must be secured against cyberattacks
for their everyday functions on the road so that safety of passengers and vehicles can be …
for their everyday functions on the road so that safety of passengers and vehicles can be …
Modeling realistic adversarial attacks against network intrusion detection systems
The incremental diffusion of machine learning algorithms in supporting cybersecurity is
creating novel defensive opportunities but also new types of risks. Multiple researches have …
creating novel defensive opportunities but also new types of risks. Multiple researches have …
A comprehensive review on deep learning algorithms: Security and privacy issues
Abstract Machine Learning (ML) algorithms are used to train the machines to perform
various complicated tasks that begin to modify and improve with experiences. It has become …
various complicated tasks that begin to modify and improve with experiences. It has become …
[HTML][HTML] A survey on neural networks for (cyber-) security and (cyber-) security of neural networks
The goal of this systematic and broad survey is to present and discuss the main challenges
that are posed by the implementation of Artificial Intelligence and Machine Learning in the …
that are posed by the implementation of Artificial Intelligence and Machine Learning in the …
Multi‐aspects AI‐based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview
IH Sarker - Security and Privacy, 2023 - Wiley Online Library
Due to the rising dependency on digital technology, cybersecurity has emerged as a more
prominent field of research and application that typically focuses on securing devices …
prominent field of research and application that typically focuses on securing devices …
Adversarial machine learning: A multilayer review of the state-of-the-art and challenges for wireless and mobile systems
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 …
normal samples but have some imperceptible noise added to them with the intention of …
[HTML][HTML] Adversarial machine learning in industry: A systematic literature review
Abstract Adversarial Machine Learning (AML) discusses the act of attacking and defending
Machine Learning (ML) Models, an essential building block of Artificial Intelligence (AI). ML …
Machine Learning (ML) Models, an essential building block of Artificial Intelligence (AI). ML …