What question did this study set out to answer?

To develop an advanced network intrusion detection system (NIDS) that effectively handles class imbalance and enhances threat detection.

March 18, 2026Open Access

WASAE-NIDS: Reverse-Frequency Class Weighting with GAN-Assisted Conditional Autoencoder for Network Intrusion Detection

Puntos clave

To develop an advanced network intrusion detection system (NIDS) that effectively handles class imbalance and enhances threat detection.
Leverage a generative adversarial network (GAN) for improved representation learning
Utilize a conditional autoencoder to adaptively process input data
Incorporate reverse-frequency class weighting to mitigate class imbalance during detection
Evaluate the proposed method on established NIDS benchmark datasets
Demonstrated superior performance in detecting diverse cyber threats compared to traditional methods
Achieved high accuracy rates, particularly for minority class threats
Highlighted improvements in NIDS robustness and overall effectiveness against evolving threats.

Resumen

Network intrusion detection systems (NIDS) are critical for maintaining the security and integrity of modern networks. Traditional IDS techniques, while effective, often struggle with the evolving nature of cyber threats and the need for real-time detection. This paper proposes WASAE-NIDS, a deep learning-based NIDS that leverages a generative adversarial network (GAN)-assisted conditional autoencoder combined with reverse-frequency class weighting to enhance detection, particularly under severe class imbalance. In evaluating NIDS benchmark datasets, our method demonstrates superior performance in detecting various types of cyber threats with high accuracy and improved performance on minority classes. The results demonstrate the potential of combining GAN-assisted representation learning and class weighting to improve NIDS robustness and effectiveness.

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