Hybrid Evolutionary Algorithms for Online Spiking Neural Networks in Streaming Anomaly Detection

Abstract

Abstract: Unsupervised anomaly discovery in stream data is a research topic with many practical applications. However, in many cases, it is not easy to collect enough training data with labeled anomalies for supervised learning of an anomaly detector in order to deploy it later for identification of real anomalies in streaming data. It is thus important to design anomalies detectors that can correctly detect anomalies without access to labeled training data. Our idea is to adapt the Online evolving Spiking Neural Network (OeSNN) classifier to the anomaly detection task. As a result, we offer an Online e

Research Question

Can hybrid evolutionary algorithms used in online evolving spiking neural networks improve convergence speed and robustness against concept drift compared to standard gradient-based adaptation in unsupervised streaming anomaly detection?

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