Unsupervised Spectrum Anomaly Detection Method for Unauthorized Bands

Yu Tian,Jing Xu,Haihua Liao,Naijin Liu,Shuai Yuan,Ya Wang

doi:10.34133/2022/9865016

Yu Tian, Jing Xu + Show 4 more

Open Access

https://doi.org/10.34133/2022/9865016

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Abstract

With the rapid development of wireless communication, spectrum plays increasingly important role in both military and civilian fields. Spectrum anomaly detection aims at detecting emerging anomaly signals and spectrum usage behavior in the environment, which is indispensable to secure safety and improve spectrum efficiency. However, spectrum anomaly detection faces many difficulties, especially for unauthorized frequency bands. In unauthorized bands, the composition of spectrum is complex and the anomaly usage patterns are unknown in prior. In this paper, a Variational Autoencoder- (VAE-) based method is proposed for spectrum anomaly detection in unauthorized bands. First of all, we theoretically prove that the anomalies in unauthorized bands will introduce Background Noise Enhancement (BNE) effect and Anomaly Signal Disappearance (ASD) effects after VAE reconstruction. Then, we introduce a novel anomaly metric termed as percentile (PER) score, which focuses on capturing the distribution variation of reconstruction error caused by ASD and BNE. In order to verify the effectiveness of our method, we developed an ISM Anomaly Detection (IAD) dataset. The proposed PER score achieves superior performance against different type of anomalies. For QPSK type anomaly, our method increases the recall rate from 80% to 93% while keeping a false alarm rate of 5%. The proposed method is beneficial to broadband spectrum sensing and massive spectrum data processing. The code will be released at git@github.com :QXSLAB/vae_ism_ano.git.

Highlights

The rapid development of radio technology has resulted in a greatly enlarged demand for spectrum resources
In order to verify the effectiveness of our method, we developed a dataset for ISM frequency band anomaly detection, termed as ISM Anomaly Detection (IAD) dataset
Similar to the approaches taken in the literature [12, 14, 16, 18], we collect ISM spectrum data in the laboratory environment as normal spectrogram samples and construct anomaly spectrogram samples by artificially sending specific types of signals to the environment

Summary

Introduction

The rapid development of radio technology has resulted in a greatly enlarged demand for spectrum resources. The signal should be detected as anomaly spectrum if it is different from these authorized systems. Authorized band anomaly detection can be well solved by signal identification, which has been well studied in recent years [5,6,7]. CHIRP 0 0 0 774 anomaly scores against different anomalous signals, we constructed four test sets. Two different VAE networks are adopted to investigate the performance of proposed PER anomaly detection score. In FC-VAE network, the 2dimensional (2D) original spectrogram sample is firstly reshaped into 1-dimensional (1D) feature. The recovered 1D feature is reshaped to generate a spectrogram with the same size as the input Both VAE networks are implemented in PyTorch and are trained by only normal samples in an unsupervised approach.

Methods

Results

Conclusion