How machine learning changes the nature of cyberattacks on IoT networks: A survey

E Bout, V Loscri, A Gallais - IEEE Communications Surveys & …, 2021 - ieeexplore.ieee.org
The Internet of Things (IoT) has continued gaining in popularity and importance in everyday
life in recent years. However, this development does not only present advantages. Indeed …

The role of artificial intelligence and machine learning in wireless networks security: Principle, practice and challenges

M Waqas, S Tu, Z Halim, SU Rehman, G Abbas… - Artificial Intelligence …, 2022 - Springer
Security is one of the biggest challenges concerning networks and communications. The
problem becomes aggravated with the proliferation of wireless devices. Artificial Intelligence …

Game theory and reinforcement learning for anti-jamming defense in wireless communications: Current research, challenges, and solutions

L Jia, N Qi, Z Su, F Chu, S Fang… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
Due to the inherently open and shared nature of the wireless channels, wireless
communication networks are vulnerable to jamming attacks, and effective anti-jamming …

6G white paper on machine learning in wireless communication networks

S Ali, W Saad, N Rajatheva, K Chang… - arXiv preprint arXiv …, 2020 - arxiv.org
The focus of this white paper is on machine learning (ML) in wireless communications. 6G
wireless communication networks will be the backbone of the digital transformation of …

Context-aware proactive content caching with service differentiation in wireless networks

S Müller, O Atan, M Van Der Schaar… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
Content caching in small base stations or wireless infostations is considered to be a suitable
approach to improve the efficiency in wireless content delivery. Placing the optimal content …

Neural fictitious self-play for radar antijamming dynamic game with imperfect information

K Li, B Jiu, W Pu, H Liu, X Peng - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
One emerging issue in modern electronic warfare is the competition between the radar and
jammer, which in principle can be viewed as a noncooperative game with two players. In …

Performance analysis of deep reinforcement learning-based intelligent cooperative jamming method confronting multi-functional networked radar

W Zhang, T Zhao, Z Zhao, D Ma, F Liu - Signal Processing, 2023 - Elsevier
With the development of artificial intelligence technology, more and more intelligent
countermeasure methods are applied in military confrontation fields to improve the …

[图书][B] White Paper on Machine Learning in 6G Wireless Communication Networks: 6G Research Visions, No. 7, 2020

A Samad, W Saad, R Nandana, C Kapseok… - 2020 - diva-portal.org
This white paper discusses various topics, advances, and projections regarding machine
learning (ML) in wireless communications. Sixth generation (6G) wireless communications …

An intelligent anti-jamming scheme for cognitive radio based on deep reinforcement learning

J Xu, H Lou, W Zhang, G Sang - IEEE Access, 2020 - ieeexplore.ieee.org
Cognitive radio network is an intelligent wireless communication system which can adjust its
transmission parameters according to the environment thanks to its learning ability. It is a …

Radar jamming decision-making in cognitive electronic warfare: A review

C Zhang, L Wang, R Jiang, J Hu, S Xu - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
With the increasingly complex electromagnetic environment and the intelligent development
of radar, the jammer, as opposed to radar, urgently needs to improve its ability to recognize …