Anomaly Detection in Satellite Communications Systems using LSTM Networks

Edward Arbon, Peter Smet, Lachlan Gunn, Mark McDonnell

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

202 Downloads (Pure)

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.
Original languageEnglish
Title of host publication2018 Military Communications and Information Systems Conference, MilCIS 2018 - Proceedings
PublisherIEEE
Number of pages6
ISBN (Electronic)9781538657607
DOIs
Publication statusPublished - 12 Dec 2018
MoE publication typeA4 Article in a conference publication
EventMilitary Communications and Information Systems Conference - Canberra, Australia
Duration: 13 Nov 201815 Nov 2018
http://www.milcis.com.au/

Conference

ConferenceMilitary Communications and Information Systems Conference
Abbreviated titleMilCIS
CountryAustralia
CityCanberra
Period13/11/201815/11/2018
Internet address

Fingerprint Dive into the research topics of 'Anomaly Detection in Satellite Communications Systems using LSTM Networks'. Together they form a unique fingerprint.

Cite this