f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks
Obtaining expert labels in clinical imaging is difficult since exhaustive annotation is time-
consuming. Furthermore, not all possibly relevant markers may be known and sufficiently …
consuming. Furthermore, not all possibly relevant markers may be known and sufficiently …
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 …
A neural network trained for prediction mimics diverse features of biological neurons and perception
Recent work has shown that convolutional neural networks (CNNs) trained on image
recognition tasks can serve as valuable models for predicting neural responses in primate …
recognition tasks can serve as valuable models for predicting neural responses in primate …
[HTML][HTML] Anomaly classification in industrial internet of things: a review
The fourth industrial revolution (Industry 4.0) has the potential to provide real-time, secure,
and autonomous manufacturing environments. The Industrial Internet of Things (IIoT) is a …
and autonomous manufacturing environments. The Industrial Internet of Things (IIoT) is a …
A radio anomaly detection algorithm based on modified generative adversarial network
X Zhou, J Xiong, X Zhang, X Liu… - IEEE Wireless …, 2021 - ieeexplore.ieee.org
Detecting ever increasing anomalous signals is critical to effective spectrum management. In
this letter, we present a radio anomaly detection algorithm based on modified generative …
this letter, we present a radio anomaly detection algorithm based on modified generative …
Unsupervised wireless spectrum anomaly detection with interpretable features
Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer
complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a …
complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a …
SAIFE: Unsupervised wireless spectrum anomaly detection with interpretable features
Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer
complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a …
complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a …
Machine learning in NextG networks via generative adversarial networks
E Ayanoglu, K Davaslioglu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Generative Adversarial Networks (GANs) are Machine Learning (ML) algorithms that have
the ability to address competitive resource allocation problems together with detection and …
the ability to address competitive resource allocation problems together with detection and …
A brief review on security issues and counter measure techniques for future generation communication system (LTE/LTE-A)
D Sagar, M Saidi Reddy - Multimedia Tools and Applications, 2024 - Springer
The article addresses the issue of current and critical protections for applications for 4G and
5G mobile phones. The objective of the article is to provide an overview of the threats to 4G …
5G mobile phones. The objective of the article is to provide an overview of the threats to 4G …
Intrusion detection for IoT devices based on RF fingerprinting using deep learning
Internet of Things (IoT) and 4G/5G wireless networks have added huge number of devices
and new services, where commercial-of-the-shelf (COTS) IoT devices have been deployed …
and new services, where commercial-of-the-shelf (COTS) IoT devices have been deployed …