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This is a Experimental Study study (also classified as: Quantitative Study).

September 16, 2025Open Access

An Integrated Hybrid Deep Learning Framework for Intrusion Detection in IoT and IIoT Networks Using CNN-LSTM-GRU Architecture

Puntos clave

The CNN-LSTM-GRU architecture achieved 100% accuracy in binary classification while improving detection granularity.
Evaluation on the CICIDS2017 dataset confirmed a generalization ability with 99.49% accuracy and high precision and recall scores.
The proposed model addresses real-world data challenges through a comprehensive preprocessing pipeline, enhancing overall performance.
Compared to traditional methods, this hybrid model demonstrates greater robustness, scalability, and adaptability in IoT and IIoT environments.

Resumen

Intrusion detection systems (IDSs) are critical for securing modern networks, particularly in IoT and IIoT environments where traditional defenses such as firewalls and encryption are insufficient against evolving cyber threats. This paper proposes an enhanced hybrid deep learning model that integrates convolutional neural networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) in a multi-branch architecture designed to capture spatial and temporal dependencies while minimizing redundant computations. Unlike conventional hybrid approaches, the proposed parallel–sequential fusion framework leverages the strengths of each component independently before merging features, thereby improving detection granularity and learning efficiency. A rigorous preprocessing pipeline is employed to handle real-world data challenges: missing values are imputed using median filling, class imbalance is mitigated through SMOTE (Synthetic Minority Oversampling Technique), and feature scaling is performed with Min–Max normalization to ensure convergence consistency. The methodology is validated on the TONIoT and CICIDS2017 dataset, chosen for its diversity and realism in IoT/IIoT attack scenarios. Three hybrid models—CNN-LSTM, CNN-GRU, and the proposed CNN-LSTM-GRU—are assessed for binary and multiclass intrusion detection. Experimental results demonstrate that the CNN-LSTM-GRU architecture achieves superior performance, attaining 100% accuracy in binary classification and 97% in multiclass detection, with balanced precision, recall, and F1-scores across all classes. Furthermore, evaluation on the CICIDS2017 dataset confirms the model’s generalization ability, achieving 99. 49% accuracy with precision, recall, and F1-scores of 0. 9954, 0. 9943, and 0. 9949, respectively, outperforming CNN-LSTM and CNN-GRU baselines. Compared to existing IDS models, our approach delivers higher robustness, scalability, and adaptability, making it a promising candidate for next-generation IoT/IIoT security.

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