Jamming attacks and anti-jamming strategies in wireless networks: A comprehensive survey
H Pirayesh, H Zeng - IEEE communications surveys & tutorials, 2022 - ieeexplore.ieee.org
Wireless networks are a key component of the telecommunications infrastructure in our
society, and wireless services become increasingly important as the applications of wireless …
society, and wireless services become increasingly important as the applications of wireless …
A survey on network security for cyber–physical systems: From threats to resilient design
Cyber-physical systems (CPS) are considered the integration of physical systems in the real
world and control software in computing systems. In CPS, the real world and the computing …
world and control software in computing systems. In CPS, the real world and the computing …
Machine learning for 6G wireless networks: Carrying forward enhanced bandwidth, massive access, and ultrareliable/low-latency service
To satisfy the expected plethora of demanding services, the future generation of wireless
networks (6G) has been mandated as a revolutionary paradigm to carry forward the …
networks (6G) has been mandated as a revolutionary paradigm to carry forward the …
Adversarial machine learning in wireless communications using RF data: A review
D Adesina, CC Hsieh, YE Sagduyu… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …
complex tasks involved in wireless communications. Supported by recent advances in …
Reinforcement learning for dynamic resource optimization in 5G radio access network slicing
The paper presents a reinforcement learning solution to dynamic resource allocation for 5G
radio access network slicing. Available communication resources (frequency-time blocks …
radio access network slicing. Available communication resources (frequency-time blocks …
Adversarial machine learning for 5G communications security
Machine learning provides automated means to capture complex dynamics of wireless
spectrum and support better understanding of spectrum resources and their efficient …
spectrum and support better understanding of spectrum resources and their efficient …
Intelligent anti-jamming communication for wireless sensor networks: A multi-agent reinforcement learning approach
Q Zhou, Y Li, Y Niu - IEEE Open Journal of the …, 2021 - ieeexplore.ieee.org
In this article, we investigate intelligent anti-jamming communication method for wireless
sensor networks. The stochastic game framework is introduced to model and analyze the …
sensor networks. The stochastic game framework is introduced to model and analyze the …
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 …
communication networks are vulnerable to jamming attacks, and effective anti-jamming …
Maintenance 5.0: towards a worker-in-the-loop framework for resilient smart manufacturing
Due to the global uncertainty caused by social problems such as COVID-19 and the war in
Ukraine, companies have opted for the use of emerging technologies, to produce more with …
Ukraine, companies have opted for the use of emerging technologies, to produce more with …
An adaptive QoS and trust-based lightweight secure routing algorithm for WSNs
A Pathak, I Al-Anbagi… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
The limited resources and low computational power of wireless sensor networks (WSNs)
make them vulnerable to various security attacks. Conventional security mechanisms …
make them vulnerable to various security attacks. Conventional security mechanisms …