Hybrid MLP-GRU Model With PCA-Optimized Feature Selection for DDoS Attack Detection in IoMT Networks

The Internet of Medical Things (IoMT) has revolutionized healthcare, but faces significant cybersecurity challenges, particularly distributed denial of service (DDoS) attacks. This research introduces a novel and efficient hybrid deep learning model for DDoS attack detection by combining a Multi-Layer Perceptron (MLP) and a gated recurring unit (GRU). The proposed framework incorporates data standardization and Principal Component Analysis (PCA) for dimensionality reduction and feature selection. The framework was evaluated using the CICIoMT2024 dataset, which specifically contains the traffic from IoMT security attacks. The experimental results demonstrate that proposed MLP-GRU model outperforms several existing classifiers and relevant models, achieving 99.99% accuracy, 100% recall, 99.99% precision and 99.99% F1 score for 6 PCA components. This hybrid model offers improved computational efficiency and robustness for IoMT systems. This research contributes to improving the security and operational integrity of IoMT systems against the growing threat of DDoS attacks.