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Related Experiment Videos

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

Michael Shi, Jiao Yin, Chee Peng Lim

IEEE Transactions on Neural Networks and Learning Systems

|July 2, 2026

Summary

This study enhances online email spam detection using Adaptive Resonance Theory (ART) models. An ensemble Fuzzy ARTMAP (FAM) model shows superior performance in adapting to evolving spam patterns and noisy data.

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Area of Science:

Cybersecurity
Artificial Intelligence
Machine Learning

Background:

Email spam detection is a critical cybersecurity challenge due to continuous, nonstationary, and noisy data streams.
Classifiers must adapt to evolving spam patterns without catastrophic forgetting.

Purpose of the Study:

To investigate online email spam detection using hybrid Adaptive Resonance Theory (ART)-based neural models.
To improve classifier robustness to noise and heterogeneous features in evolving data streams.

Main Methods:

Utilized Fuzzy ARTMAP (FAM) for its stability-plasticity properties, augmented with preprocessing and postprocessing techniques.
Compared concatenated and modular feature representations with FAM and Fusion ARTMAP (FusAM) architectures.
Employed an ensemble majority voting method to reduce prediction errors.

Main Results:

ART-based paradigms showed different accuracy-efficiency trade-offs.
Feature extraction and ensemble methods consistently enhanced model robustness against noise.
The ensemble FAM model achieved the best overall performance.

Conclusions:

Hybrid ART-based models, particularly ensemble FAM, are effective for online email spam detection.
Ensemble methods and feature engineering are crucial for robust performance in evolving, noisy data streams.
Demonstrated the utility of ART models in adaptive cybersecurity applications.