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AD-NEv: A Scalable Multilevel Neuroevolution Framework for Multivariate Anomaly Detection.

Marcin Pietron, Dominik Zurek, Kamil Faber

    IEEE Transactions on Neural Networks and Learning Systems
    |August 14, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Neuroevolution automates neural network optimization for anomaly detection. The proposed Anomaly Detection Neuroevolution (AD-NEv) framework efficiently optimizes feature subspaces, model architectures, and network weights for superior performance in multivariate time-series anomaly detection.

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    Area of Science:

    • Cyberphysical systems
    • Failure prediction
    • Machine learning

    Background:

    • Deep learning models are crucial for anomaly detection but require time-consuming optimization.
    • Existing neuroevolution methods often overlook feature subspaces and model weights, limiting optimization scope.

    Purpose of the Study:

    • To introduce Anomaly Detection Neuroevolution (AD-NEv), a scalable, multilevel framework for optimizing anomaly detection in multivariate time-series data.
    • To synergistically optimize feature subspaces, model architectures, and network weights for enhanced anomaly detection.

    Main Methods:

    • AD-NEv employs a bagging technique to optimize feature subspaces for ensemble models.
    • It integrates architecture search with non-gradient fine-tuning of network weights.
    • The framework is designed for scalability, particularly with multiple Graphics Processing Units (GPUs).

    Main Results:

    • Models generated by AD-NEv demonstrate superior performance compared to established deep learning architectures on benchmark datasets.
    • The framework efficiently automates the entire optimization process.
    • AD-NEv exhibits high scalability and performance gains when utilizing multiple GPUs.

    Conclusions:

    • AD-NEv offers an effective and efficient automated solution for multivariate time-series anomaly detection.
    • The multilevel optimization approach, considering features, architecture, and weights, significantly enhances detection accuracy.
    • The framework's scalability makes it suitable for large-scale, real-world applications.