Toward Robust IDS in Network Security: Handling Class Imbalance With Deep Hybrid Architectures
Saransh Shanka, Divjot Singh, Anurag Badoni, Mahendra Kumar Shukla, Om Jee Pandey, Nadjib Aitsaadi · IEEE Networking Letters · 2025
The growing sophistication of cyberattacks demands Intrusion Detection Systems (IDS) that are both accurate and adaptive to diverse network threats. Traditional IDS often suffer degraded performance due to high-dimensional features and severe class imbalance in network traffic datasets. To address these issues, we propose a hybrid IDS framework integrating four optimized models (XGBoost, Long Short-Term Memory, MiniVGGNet, and AlexNet) enhanced through Random Forest Regressor-based feature selection and the Difficult Set Sampling Technique (DSSTE) for class balancing. Two integration strategies are employed: a hard-voting Ensemble and a Mixture of Experts (MoE) with a gating network for adaptive weighting. Comprehensive hyperparameter tuning via Keras Tuner and RandomizedSearchCV maximizes model performance. Evaluated on the CICIDS-2017 dataset, the system achieves detection rates above 99% with micro-average AUC values near 1.0, demonstrating strong generalization and effectiveness in detecting both majority and minority intrusions. The proposed framework holds strong relevance for security-critical domains, particularly wireless health monitoring systems, where ensuring the confidentiality and integrity of sensitive data is vital, thereby underscoring its suitability for real-world deployment.