Work

A generative adversarial network-augmented dual-stream Mamba-transformer framework for advanced intrusion detection and cyber threat mitigation in smart healthcare networks

Year: 2026

Type: article

Abstract: The rapid proliferation of the Internet of Medical Things (IoMT) has dramatically expanded the attack surface of smart healthcare networks, with cyber-threats including DDoS, spoofing, reconnaissance, and ransomware targeting IoMT infrastructures and jeopardizing patient safety. Traditional intrusion detection systems (IDS) fail to address protocol heterogeneity, high-dimensional traffic data, and severe class imbalance inherent in IoMT environments. This paper proposes GAN-Mamba Former-IDS, a novel intrusion detection framework for advanced cyber threat detection in smart healthcare networks. Three core innovations are integrated: (i) a Protocol-Aware Feature Router partitioning network traffic features into four semantically coherent protocol groups; (ii) a dual-stream Mamba-Transformer encoder concurrently capturing sequential dependencies via a linear-time state-space model and global feature interactions via multi-head self-attention; and (iii) Conditional Tabular GAN (CTGAN)-based augmentation addressing minority attack class imbalance. All experiments were implemented in PyTorch 2.1 on an NVIDIA A100 40GB GPU. The proposed framework achieves 97.71% accuracy, 0.7901 macro-F1 score, and 0.9842 ROC-AUC on the 19-class CICIoMT2024 test set, outperforming baseline models including MLP, 1D-CNN, BiLSTM, Transformer-only, and Mamba-only. CTGAN augmentation improves minority-class macro-F1 by up to +0.2109 for the rarest attack classes. Results are confirmed across five random seeds (mean ± std: Accuracy = 97.71 ± 0.38%, Macro-F1 = 0.7901 ± 0.014). GAN-Mamba Former-IDS provides a scalable, reproducible, and effective cybersecurity solution for next-generation IoMT environments, with demonstrated generalization across five heterogeneous IoT security benchmarks.

Source: Scientific Reports

DOI: 10.1038/s41598-026-74494-4

Authors: Purushottam Kumar, Sanjay Kumar, Indrajit Mukherjee

Institution: Birla Institute of Technology, Mesra

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,690