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
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 …
Anomaly detection based on a dynamic Markov model
H Ren, Z Ye, Z Li - Information Sciences, 2017 - Elsevier
Anomaly detection in sequence data is becoming more and more important in a wide variety
of application domains such as credit card fraud detection, health care in medical field, and …
of application domains such as credit card fraud detection, health care in medical field, and …
AI-based abnormality detection at the PHY-layer of cognitive radio by learning generative models
Introducing a data-driven Self-Awareness (SA) module in Cognitive Radio (CR) can support
the system to establish secure networks against various attacks from malicious users. Such …
the system to establish secure networks against various attacks from malicious users. Such …
An Anomaly Detection Method for Wireless Sensor Networks Based on the Improved Isolation Forest
J Chen, J Zhang, R Qian, J Yuan, Y Ren - Applied Sciences, 2023 - mdpi.com
With the continuous development of technologies such as the Internet of Things (IoT) and
cloud computing, sensors collect and store large amounts of sensory data, realizing real …
cloud computing, sensors collect and store large amounts of sensory data, realizing real …
Scaling deep learning models for spectrum anomaly detection
Spectrum management in cellular networks is a challenging task that will only increase in
difficulty as complexity grows in hardware, configurations, and new access technology (eg …
difficulty as complexity grows in hardware, configurations, and new access technology (eg …
Crowdsourced wireless spectrum anomaly detection
Automated wireless spectrum monitoring across frequency, time and space will be essential
for many future applications. Manual and fine-grained spectrum analysis is becoming …
for many future applications. Manual and fine-grained spectrum analysis is becoming …
Efficient generative wireless anomaly detection for next generation networks
Anomaly detection in wireless signals through multi-sensor fusion has numerous real-world
applications including spectrum monitoring and awareness, fault detection, and spectrum …
applications including spectrum monitoring and awareness, fault detection, and spectrum …
Spectrum anomaly detection based on spatio-temporal network prediction
C Peng, W Hu, L Wang - Electronics, 2022 - mdpi.com
With the miniaturization of communication devices, the number of distributed
electromagnetic devices is increasing. In order to achieve effective management of the …
electromagnetic devices is increasing. In order to achieve effective management of the …