MIDS-GAN: Minority Intrusion Data Synthesizer GAN—An ACON Activated Conditional GAN for Minority Intrusion Detection

Chalerm Klinkhamhom, Pongsarun Boonyopakorn () and Pongpisit Wuttidittachotti
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Chalerm Klinkhamhom: Department of Network and Information Security Management, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand
Pongsarun Boonyopakorn: Department of Network and Information Security Management, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand
Pongpisit Wuttidittachotti: Department of Network and Information Security Management, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand

Mathematics, 2025, vol. 13, issue 21, 1-25

Abstract: Intrusion Detection Systems (IDS) are vital to cybersecurity but suffer from severe class imbalance in benchmark datasets such as NSL-KDD and UNSW-NB15. Conventional oversampling methods (e.g., SMOTE, ADASYN) are efficient yet fail to preserve the latent semantics of rare attack behaviors. This study introduces the Minority-class Intrusion Detection Synthesizer GAN (MIDS-GAN), a divergence-minimization framework for minority data augmentation under structured feature constraints. MIDS-GAN integrates (i) correlation-based structured feature selection (SFS) to reduce redundancy, (ii) trainable ACON activations to enhance generator expressiveness, and (iii) KL-divergence-guided alignment to ensure distributional fidelity. Experiments on NSL-KDD and UNSW-NB15 demonstrate significant improvement on detection, with recall increasing from 2% to 27% for R2L and 1% to 17% for U2R in NSL-KDD, and from 18% to 44% for Worms and 69% to 75% for Shellcode in UNSW-NB15. Weighted F1-scores also improved to 78%, highlighting MIDS-GAN’s effectiveness in enhancing minority-class detection through a principled, divergence-aware approach.

Keywords: intrusion detection systems; cybersecurity; Generative Adversarial Networks; data balancing; minority-class synthesis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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