An EfficientNet hybrid deep learning detection framework of network intrusion with an optimized mixture-of-experts architecture.

The growing interconnectedness of the modern networked products has in turn resulted in a proportional growth. This is in the level of advanced cyber threats and therefore the need to develop intrusion detection systems which is not just accurate but also computationally effective and responsive to various operational factors. This paper proposes a deep learning-based framework for network intrusion detection using a mixture-of-experts (MoE) structure, along with EfficientNet-based transfer learning. This study also proposes a refined addax optimization algorithm (RAOA), which is the modified version of existing addax optimization algorithm, to tune the hyper-parameters under resource limited situations. The validity of the framework was actually tested on a rigorous performance assessment based on a comprehensive benchmark dataset comprising both network traffic and sensor records. A detailed comparison with 7 state-of-the-art models indicates that the proposed method provides higher macro F1-score of 95.7%. Moreover, the model has a low inference latency of 4.7 ms and a small model size of 21 MB that highlights its appropriateness of using it in resource-constrained environments. These results indicate that transfer learning, modular network design, and intelligent optimization could be used together to build the most effective and practical intrusion detection systems applicable to most network security applications.