# MAGE-ID: A Multimodal Generative Framework for Intrusion Detection Systems

arXiv: https://arxiv.org/abs/2512.03375 · HTML: https://mlsanity.com/paper/2512.03375/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 3 Dec 2025.

## Builds on (most cited first, 5 of 5)

- [Reliable Fidelity and Diversity Metrics for Generative Models](https://mlsanity.com/paper/2002.09797/) (2020)
- [TabDDPM: Modelling Tabular Data with Diffusion Models](https://mlsanity.com/paper/2209.15421/) (2022)
- [Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space](https://mlsanity.com/paper/2310.09656/) (2023)
- [Variational Autoencoder-Based Black-Box Adversarial Attack on Collaborative DNN Inference](https://mlsanity.com/paper/2508.01107/) (2025)
- [Handling Uncertainty in Health Data using Generative Algorithms](https://mlsanity.com/paper/2503.03715/) (2025)

## Built on by (most cited first, 2 of 2)

- [Noise-Aware Misclassification Attack Detection in Collaborative DNN Inference](https://mlsanity.com/paper/2603.17914/) (2026)
- [Multi-Modal Anomaly Detection: A Survey](https://mlsanity.com/paper/2608.24937/) (2026)

## Abstract (arXiv)

Modern Intrusion Detection Systems (IDS) face severe challenges due to heterogeneous network traffic, evolving cyber threats, and pronounced data imbalance between benign and attack flows. While generative models have shown promise in data augmentation, existing approaches are limited to single modalities and fail to capture cross-domain dependencies. This paper introduces MAGE-ID (Multimodal Attack Generator for Intrusion Detection), a diffusion-based generative framework that couples tabular flow features with their transformed images through a unified latent prior. By jointly training Transformer and CNN-based variational encoders with an EDM style denoiser, MAGE-ID achieves balanced and coherent multimodal synthesis. Evaluations on CIC-IDS-2017 and NSL-KDD demonstrate significant improvements in fidelity, diversity, and downstream detection performance over TabSyn and TabDDPM, highlighting the effectiveness of MAGE-ID for multimodal IDS augmentation.