Anomaly Detection in Satellite Communications Systems using LSTM Networks

Abstract

Most satellite communications monitoring tools use simple thresholding of univariate measurements to alert the operator to unusual events [1] [2]. This approach suffers from frequent false alarms, and is moreover unable to detect sequence or multivariate anomalies [3]. Here we consider the problem of detecting outliers in high-dimensional time-series data, such as transponder frequency spectra. Long Short Term Memory (LSTM) networks are able to form sophisticated representations of such multivariate temporal data, and can be used to predict future sequences when presented with sufficient context. We report here on the utility of LSTM prediction error as a defacto measure for detecting outliers. We show that this approach significantly improves on simple threshold models, as well as on moving average and static predictors. The latter simply assume the next trace will be equal to the previous trace. The advantages of using an LSTM network for anomaly detection are twofold. Firstly, the training data do not need to be labelled. This alleviates the need to provide the model with specific examples of anomalies. Secondly, the trained model is able to detect previously unseen anomalies. Such anomalies have a degree of unpredictability that makes them stand out. LSTM networks are further able to potentially detect more nuanced sequence and multivariate anomalies. These occur when all values are within normal tolerances, but the sequence or combinations of values are themselves unusual. The technique we describe could be used in practice for alerting satellite network operators to unusual conditions requiring their attention.

Deep Learning-Based Anomaly Detection in Cyber-Physical Systems: Progress and Opportunities

“…Characteristics of DLAD methods in aerial systems. There are methods studying faults in aircraft [72] and spacecraft [37,44,92]. The faults consist of point and contextual anomalies in sensor and communication data.…”

Spectrum Anomaly Detection Based on Spatio-Temporal Network Prediction

“…We artificially add anomalies to the pure electromagnetic spectrum data to solve this problem. In this paper, four spectrum anomalies are generated by referring to the literature [13,16], which are (i) tone-anomaly: continuous interference is made up of one or more sinusoids with a random center frequency; (ii) pulse-anomaly: time-pulsed signal having a variable start and end time; (iii) chirp-anomaly: chirp signal with a variable center frequency and hopping rate; (iv) wpulse-anomaly: wideband signals that pulse over the whole frequency spectrum. In order to detect the pervasiveness of the algorithm, the variety of anomalous states is increased, and in addition to the above four interference anomalies, any two anomalies are randomly selected for mixing so that the variety of anomalous states increases to 10.…”

“…LSTM networks can detect a variety of abnormal states in the LTE signal spectrum by predicting spectral data and can detect anomalies across the frequency band by transfer learning [15]. In order to solve the abnormal spectrum status in satellite communication, LSTM networks can realize the detection of multi-variable abnormal states and subtle abnormal states on the basis of spectrum prediction [16]. The encoder is combined with the GAN network to form a new anomaly detection network.…”