| Title | Deep Learning for Spectrum Anomaly Detection in Cognitive mmWave Radios |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Toma, A., Krayani, A., Marcenaro, L., Gao, Y., Regazzoni, C. S. |
| Conference Name | 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications |
| Keywords | AC-GAN, anomaly detection, auxiliary classifier generative adversarial network, Cognitive mmWave Radios, Cognitive radio, Cognitive Radio Security, Cognitive Radios, Communication system security, complex dynamic radio environment, conditional generative adversarial network, deep generative models, Deep Learning, dynamic radio spectrum, feature extraction, Gallium nitride, generative adversarial networks, Generative Models, Internet of Things, learning (artificial intelligence), Millimeter Wave, Millimeter Wave band, millimetre wave communication, mmWave dataset, mmWave spectrum, neural nets, probability, pubcrawl, radio spectrum management, Resiliency, secure radios, shared access medium, spectrum anomalies, spectrum anomaly detection, telecommunication computing, telecommunication security, Training, variational auto encoder, Wireless communication |
| Abstract | Millimeter Wave (mmWave) band can be a solution to serve the vast number of Internet of Things (IoT) and Vehicle to Everything (V2X) devices. In this context, Cognitive Radio (CR) is capable of managing the mmWave spectrum sharing efficiently. However, Cognitive mmWave Radios are vulnerable to malicious users due to the complex dynamic radio environment and the shared access medium. This indicates the necessity to implement techniques able to detect precisely any anomalous behaviour in the spectrum to build secure and efficient radios. In this work, we propose a comparison framework between deep generative models: Conditional Generative Adversarial Network (C-GAN), Auxiliary Classifier Generative Adversarial Network (AC-GAN), and Variational Auto Encoder (VAE) used to detect anomalies inside the dynamic radio spectrum. For the sake of the evaluation, a real mmWave dataset is used, and results show that all of the models achieve high probability in detecting spectrum anomalies. Especially, AC-GAN that outperforms C-GAN and VAE in terms of accuracy and probability of detection. |
| DOI | 10.1109/PIMRC48278.2020.9217240 |
| Citation Key | toma_deep_2020 |
Deep Learning for Spectrum Anomaly Detection in Cognitive mmWave Radios