A Spiking Neural Network Based Auto-encoder for Anomaly Detection in Streaming Data

Abstract

Anomaly Detection (AD) is useful for a range of applications including cyber security, health analytics, robotics, defense and big data. Automating the detection of anomalies is necessary to deal with large volumes of data and to satisfy real time processing constraints. Current Machine Learning (ML) methods have had some success in the automated detection of anomalies, but no ideal ML solutions have been found for any domain. Spiking Neural Networks (SNNs), an emerging ML technique, have the potential to do AD well, especially for Edge applications where it needs to be low power, readily adaptable, autonomous and reliable. Here we investigate SNNs doing anomaly detection on streams of text. We show that SNNs are well suited for detecting anomalous character sequences, that they can learn rapidly, and that there are many optimizations to the SNN architecture and training that can improve AD performance.