Cybersecurity is a critical concern for next-generation data centers, where fast and reliable Intrusion Detection Systems (IDS) are essential. While traditional deep learning models deliver strong performance, they often face limitations related to data privacy, latency, and dependence on centralized architectures. Accordingly, we propose FedTran, a novel IDS pipeline that supports collaborative learning by leveraging secure model updates and shared learned representations, while rigorously ensuring privacy preservation and data confidentiality. First, data imbalance is handled using SMOTE–Tomek Links, followed by feature selection through metaheuristic optimization techniques, specifically the Growth Optimizer Algorithm (GOA), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO). Second, FedTran adopts a kappa-weighted fusion approach to integrate pretrained ResNet50, InceptionV3, and DenseNet121 models within a transfer learning (TL) framework. By mapping tabular features to structured RGB images, the proposed IDS model facilitates hierarchical and spatial feature learning using CNN-based architectures. Third, Federated Averaging (FedAvg) facilitates decentralized, privacy-preserving collaborative training across distributed edge nodes. In this setting, improved generalization and reduced overfitting are achieved through complementary mechanisms, including data distribution handling, regularization techniques, early stopping, and ensemble learning, thereby limiting sensitivity to dataset-specific noise. On the UNSW-NB15 dataset, the proposed method achieves 96.06% accuracy with FedAvg and 99.98% accuracy with the TL model, significantly surpassing the conventional CNN in both predictive accuracy and training efficiency. Furthermore, the integration of a SHAP-based XAI module allows FedTran to maintain high detection accuracy while offering transparent and interpretable insights for effective security decision-making.
Marwan et al. (Tue,) studied this question.