Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks

In this work, we propose a novel OeSNN-UAD (Online evolving Spiking Neural Networks for Unsupervised Anomaly Detection) approach for online anomaly detection in univariate time series data. Our approach is based on evolving Spiking Neural Networks (eSNN). Its distinctive feature is that the proposed eSNN architecture learns in the process of classifying input values to be anomalous or not. In fact, we offer an unsupervised learning method for eSNN, in which classification is carried out without earlier pre-training of the network with data with labeled anomalies. Unlike in a typical eSNN architecture, neurons in the output repository of our architecture are not divided into known a priori decision classes. Each output neuron is assigned its own output value, which is modified in the course of learning and classifying the incoming input values of time series data. To better adapt to the changing characteristic of the input data and to make their classification efficient, the number of output neurons is limited: the older neurons are replaced with new neurons whose output values and synapses' weights are adjusted according to the current input values of the time series. The proposed OeSNN-UAD approach was experimentally compared to the state-of-the-art unsupervised methods and algorithms for anomaly detection in stream data. The experiments were carried out on Numenta Anomaly Benchmark and Yahoo Anomaly Datasets. According to the results of these experiments, our approach significantly outperforms other solutions provided in the literature in the case of Numenta Anomaly Benchmark. Also in the case of real data files category of Yahoo Anomaly Benchmark, OeSNN-UAD outperforms other selected algorithms, whereas in the case of Yahoo Anomaly Benchmark synthetic data files, it provides competitive results to the results recently reported in the literature.