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 …
A survey on device behavior fingerprinting: Data sources, techniques, application scenarios, and datasets
In the current network-based computing world, where the number of interconnected devices
grows exponentially, their diversity, malfunctions, and cybersecurity threats are increasing at …
grows exponentially, their diversity, malfunctions, and cybersecurity threats are increasing at …
Deep learning in security of internet of things
Y Li, Y Zuo, H Song, Z Lv - IEEE Internet of Things Journal, 2021 - ieeexplore.ieee.org
Internet-of-Things (IoT) technology is increasingly prominent in the current stage of social
development. All walks of life have begun to implement the IoT integration technology, so as …
development. All walks of life have begun to implement the IoT integration technology, so as …
Machine learning for the detection and identification of Internet of Things devices: A survey
The Internet of Things (IoT) is becoming an indispensable part of everyday life, enabling a
variety of emerging services and applications. However, the presence of rogue IoT devices …
variety of emerging services and applications. However, the presence of rogue IoT devices …
DeepLoRa: Fingerprinting LoRa devices at scale through deep learning and data augmentation
A Al-Shawabka, P Pietraski, SB Pattar… - Proceedings of the …, 2021 - dl.acm.org
The Long Range (LoRa) protocol for low-power wide-area networks (LPWANs) is a strong
candidate to enable the massive roll-out of the Internet of Things (IoT) because of its low …
candidate to enable the massive roll-out of the Internet of Things (IoT) because of its low …
Specific emitter identification with limited samples: A model-agnostic meta-learning approach
N Yang, B Zhang, G Ding, Y Wei, G Wei… - IEEE …, 2021 - ieeexplore.ieee.org
It is necessary but difficult to obtain a large number of labeled samples to train the
classification model in many real scenes. This letter proposes an approach for specific …
classification model in many real scenes. This letter proposes an approach for specific …
A survey on machine learning-based performance improvement of wireless networks: PHY, MAC and network layer
This paper presents a systematic and comprehensive survey that reviews the latest research
efforts focused on machine learning (ML) based performance improvement of wireless …
efforts focused on machine learning (ML) based performance improvement of wireless …
Radio identity verification-based IoT security using RF-DNA fingerprints and SVM
D Reising, J Cancelleri, TD Loveless… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
It is estimated that the number of Internet-of-Things (IoT) devices will reach 75 billion in the
next five years. Most of those currently and soon-to-be deployed devices lack sufficient …
next five years. Most of those currently and soon-to-be deployed devices lack sufficient …
Iotargos: A multi-layer security monitoring system for internet-of-things in smart homes
The wide deployment of IoT systems in smart homes has changed the landscape of
networked systems, Internet traffic, and data communications in residential broadband …
networked systems, Internet traffic, and data communications in residential broadband …
PAST-AI: Physical-layer authentication of satellite transmitters via deep learning
Physical-layer security is regaining traction in the research community, due to the
performance boost introduced by deep learning classification algorithms. This is particularly …
performance boost introduced by deep learning classification algorithms. This is particularly …