Recurrent Neural Radio Anomaly Detection

2016/11/01 by Timothy J. O’Shea, O'Shea, Timothy J, T. Charles Clancy +3 · 3 citations
Computer Science · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Network Security and Intrusion Detection #Wireless Signal Modulation Classification

paper · pdf · doi:10.48550/arxiv.1611.00301

openalex publication_date 2016/11/01 · openalex created_date 2016/11/11 · openalex updated_date 2026/07/28

Abstract

We introduce a powerful recurrent neural network based method for novelty detection to the application of detecting radio anomalies. This approach holds promise in significantly increasing the ability of naive anomaly detection to detect small anomalies in highly complex complexity multi-user radio bands. We demonstrate the efficacy of this approach on a number of common real over the air radio communications bands of interest and quantify detection performance in terms of probability of detection an false alarm rates across a range of interference to band power ratios and compare to baseline methods.

Citations

• Recurrent Neural Network Regularization

Cited by

• Sequential Anomaly Detection using Inverse Reinforcement Learning
• The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications
• Sparse Array Capon Beamformer Design Availing Deep Learning

Related

• Deep Predictive Coding Neural Network for RF Anomaly Detection in\n Wireless Networks
• Detecting radio frequency interference in radio‐antenna arrays with the recurrent neural network algorithm
• Online Reliable Anomaly Detection via Neuromorphic Sensing and Communications
• Active Rule Mining for Multivariate Anomaly Detection in Radio Access Networks
• Spiking Neural Networks for Radio Frequency Interference Detection in Radio Astronomy