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 …
Physical layer security for authentication, confidentiality, and malicious node detection: a paradigm shift in securing IoT networks
The pervasiveness of commercial Internet of Things (IoT) around the globe is expected to
reach significant levels with the upcoming sixth generation of mobile networks (6G) …
reach significant levels with the upcoming sixth generation of mobile networks (6G) …
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 …
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 …
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 …
A survey on IoT profiling, fingerprinting, and identification
The proliferation of heterogeneous Internet of things (IoT) devices connected to the Internet
produces several operational and security challenges, such as monitoring, detecting, and …
produces several operational and security challenges, such as monitoring, detecting, and …
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 …