A hybrid ABC-based online evolving spiking neural network for unsupervised anomaly detection in streaming data

The demand for robust unsupervised anomaly detection in streaming data has grown significantly in the era of smart devices, where vast amounts of data are continuously collected from such devices. Leveraging this data through effective anomaly detection is essential and necessitates a system that can work in real-time. One of the most innovative solutions is the Online Evolving Spiking Neural Network (OeSNN). The OeSNN offers a robust framework for knowledge discovery in streaming data since it can evolve and adapt to new data patterns in real-time, thereby eliminating the need for retraining. However, reliance on manual hyperparameter tuning presents notable challenges for OeSNN that can compromise model accuracy and stability. To address these challenges, this work introduces a novel hybrid approach (HABCOeSNN) which combines the Artificial Bee Colony (ABC) algorithm with Online Evolving Spiking Neural Networks (OeSNN). The HABCOeSNN extensively investigates the optimization of five key hyperparameters, including window size ( Wsize ), anomaly classification factor ( ε ), similarity value ( SIM ), modulation factor ( MOD ), and threshold factor ( C ). The proposed method was thoroughly evaluated on two benchmark datasets, the Numenta Anomaly Benchmark (NAB) and Yahoo Webscope, using a comprehensive set of evaluation metrics. To ensure a more accurate evaluation, we employed a Multi-Criteria Decision-Making (MCDM) approach to validate performance across multiple criteria, rather than relying on a single metric. Further comparative analyses were conducted against five well-established optimization algorithms: Particle Swarm Optimization (PSO), Flower Pollination Algorithm (FPA), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and Grid Search (GS), as well as traditional classifiers, Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN). An ablation study was performed to assess the individual contributions of the HABCOeSNN components. The experimental results demonstrate that HABCOeSNN achieves superior performance across diverse time series, with F1-scores ranging from 0.749 to 0.942 on Yahoo Webscope and from 0.427 to 0.878 on the NAB. The results reveal that HABCOeSNN consistently outperforms a wide range of baselines in terms of accuracy and reliability, as confirmed by a statistical one-way analysis of variance (ANOVA) test. These findings highlight the crucial role of automated hyperparameter optimization in improving the performance of OeSNN for unsupervised anomaly detection in streaming data environments.