Enhancing IoT Cybersecurity with Multi-Layer Deep Transfer Learning Approach for Intrusion Detection

Intrusion detection in IoT-enabled cloud environments is challenged by high-dimensional traffic, class imbalance, and limited labeled data. This paper proposes a hybrid framework combining Golden Jackal–Grey Wolf Optimization (GJO-GWO) for feature selection with a Kernel Mean Alignment Autoencoder (KMA-AE) for deep transfer learning. GJO-GWO selects a compact, discriminative feature subset, while KMA-AE aligns source and target latent representations to mitigate distribution mismatch. Experiments on the CIDDS-001 dataset achieve 90.21% accuracy and 0.90 macro-F1, with improved precision–recall for minority attacks and a 60% feature reduction. Although training is more expensive, the method attains the lowest inference time, enabling real-time deployment. Overall, the framework provides an effective and generalizable intrusion detection solution for dynamic IoT environments.