Reinforcement learning-based physical cross-layer security and privacy in 6G
Sixth-generation (6G) cellular systems will have an inherent vulnerability to physical (PHY)-
layer attacks and privacy leakage, due to the large-scale heterogeneous networks with …
layer attacks and privacy leakage, due to the large-scale heterogeneous networks with …
A systematic review on Deep Learning approaches for IoT security
The constant spread of smart devices in many aspects of our daily life goes hand in hand
with the ever-increasing demand for appropriate mechanisms to ensure they are resistant …
with the ever-increasing demand for appropriate mechanisms to ensure they are resistant …
Optimal privacy preservation strategies with signaling Q-learning for edge-computing-based IoT resource grant systems
S Shen, X Wu, P Sun, H Zhou, Z Wu, S Yu - Expert Systems with …, 2023 - Elsevier
Data privacy leakage can be severe when a malicious Internet of Things (IoT) node sends
requests to gather private data from an edge-computing-based IoT cloud storage system …
requests to gather private data from an edge-computing-based IoT cloud storage system …
A survey of defensive deception: Approaches using game theory and machine learning
Defensive deception is a promising approach for cyber defense. Via defensive deception, a
defender can anticipate and prevent attacks by misleading or luring an attacker, or hiding …
defender can anticipate and prevent attacks by misleading or luring an attacker, or hiding …
[HTML][HTML] Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes
The fast proliferation of edge devices for the Internet of Things (IoT) has led to massive
volumes of data explosion. The generated data is collected and shared using edge-based …
volumes of data explosion. The generated data is collected and shared using edge-based …
Enabling AI in future wireless networks: A data life cycle perspective
Recent years have seen rapid deployment of mobile computing and Internet of Things (IoT)
networks, which can be mostly attributed to the increasing communication and sensing …
networks, which can be mostly attributed to the increasing communication and sensing …
Mobility based trust evaluation for heterogeneous electric vehicles network in smart cities
Smart cities can manage assets and resources efficiently by using different types of
electronic data collection sensors, devices and vehicles. However, growing complexity of …
electronic data collection sensors, devices and vehicles. However, growing complexity of …
A privacy-protected intelligent crowdsourcing application of IoT based on the reinforcement learning
The crowdsourcing scheme emerges as a promising solution for data-based application in
the Internet of Things (IoT) network by dividing the large-scale complex sensing tasks into …
the Internet of Things (IoT) network by dividing the large-scale complex sensing tasks into …
Industrial internet-of-things security enhanced with deep learning approaches for smart cities
N Magaia, R Fonseca, K Muhammad… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
The significant evolution of the Internet of Things (IoT) enabled the development of
numerous devices able to improve many aspects in various fields in the industry for smart …
numerous devices able to improve many aspects in various fields in the industry for smart …
An incentive mechanism for privacy-preserving crowdsensing via deep reinforcement learning
With the rise of the Internet of Things (IoT), the number of mobile devices with sensing and
computing capabilities increases dramatically, paving the way toward an emerging …
computing capabilities increases dramatically, paving the way toward an emerging …