Quantifying Robustness of Hybrid Evolutionary Online Spiking Neural Networks Against Concept Drift in Multimodal Streaming Data

Abstract

Abstract: While conventional Intrusion Detection Systems (IDS) are essential for defending against intruders in the Industrial Internet of Things (IIoT), handling data from heterogeneous and streaming data sources should receive more attention. This work introduces a novel Optimized IForest-based Intrusion Detection System (OIFIDS) which is designed to handle both heterogeneous and streaming data efficiently. The suggested approach employs a collection of optimized binary trees, each of which is trained on a distinctive subset of data, and in which the location of empty leaves determines the anomaly sco

Research Question

Can the robustness of online evolving spiking neural networks with hybrid evolutionary adaptation be quantitatively measured against concept drift in multimodal streaming data using metrics like F1-score and precision-recall curves?

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