AMoE-IDS: An Adaptive Mixture-of-Experts Framework for Cross-Dataset Intrusion Detection

Abstract

Intrusion Detection Systems (IDS) are essential for securing modern network infrastructures against increasingly sophisticated cyber threats. While deep learning-based IDS have shown promising performance, most existing approaches rely on static and monolithic architectures that struggle to adapt to heterogeneous environments such as Internet of Things (IoT) systems, enterprise networks, and mixed traffic scenarios. Moreover, conventional ensemble and hybrid methods typically employ fixed fusion strategies, limiting their ability to exploit input-dependent specialization. To address these limitations, this paper proposes an Adaptive Mixture-of-Experts Intrusion Detection System (AMoE-IDS), a hybrid deep learning framework that integrates a shared feature encoder, multiple specialized expert networks, and an adaptive gating mechanism. The shared encoder learns a unified latent representation from heterogeneous feature spaces, while the gating network dynamically routes each input to the most relevant experts, enabling conditional computation and improved detection performance. Extensive experiments conducted on three recent benchmark datasets, CICIoT2023, CSE-CICIDS 2018, and TII-SSRC-23, demonstrate that AMoE-IDS consistently outperforms conventional deep learning and hybrid IDS models. The proposed framework achieves F1-scores of 99.19%, 99.67%, and 99.68% and AUC values of 0.992, 0.991, and 0.990 on CICIoT2023, CSE-CICIDS 2018, and TII-SSRC-23, respectively. Despite its multi-expert architecture, the model maintains low inference latency ranging from 1.28 to 1.56 ms per network flow, supporting real-time deployment. Cross-dataset evaluation confirms the robustness of AMoE-IDS under distribution shifts, while ablation studies highlight the critical role of feature harmonization and adaptive expert selection. Statistical significance analysis further validates the reliability of the observed improvements. Overall, the proposed framework demonstrates competitive performance, good scalability, and improved cross-dataset generalization.