What question did this study set out to answer?

To develop a deep learning-based intrusion detection system that enhances network security for IoT environments by optimizing feature selection and addressing data imbalance.

April 25, 2026Open Access

Enhancing IoT Network Security: A BPSO-Optimized Attention-GRU Deep Learning Framework for Intrusion Detection

Key Points

To develop a deep learning-based intrusion detection system that enhances network security for IoT environments by optimizing feature selection and addressing data imbalance.
Proposed a hybrid IDS integrating BPSO for feature optimization and MHA with GRU for deep learning architecture.
Utilized Target Sampling to balance class distributions in network traffic datasets.
Evaluated model performance using BoT-IoT and CIC-IDS2017 benchmark datasets.
Achieved a recall of 98.42% and 99.76% with F1-scores of 98.94% and 99.76% for binary classification on the BoT-IoT and CIC-IDS2017 datasets.
For multiclass classification, recall rates were 99.79% and 98.69% with F1-scores of 99.89% and 98.04%, respectively.
Demonstrated superior performance compared to recent state-of-the-art IDSs.

Abstract

The exponential expansion of computer networks, alongside the rapid development of the Internet of Things (IoT), has significantly increased the volume and complexity of transmitted data, emphasizing the need for robust network security measures to secure sensitive data and prevent unauthorized access or breaches. Intrusion Detection Systems (IDSs) have emerged as a vital tool for protecting networks and IoT environments from threats. Various IDSs have been proposed in the literature; however, the lack of optimal feature learning, computational efficiency, and reliance on obsolete datasets poses significant challenges, limiting their effectiveness against evolving cyber threats. Moreover, traditional IDSs struggle to efficiently manage the high-dimensional and imbalanced nature of IoT network traffic data. To address these challenges, this research proposes a hybrid deep learning (DL)-based IDS integrating Binary Particle Swarm Optimization (BPSO), MultiHead Attention mechanisms (MHA), and a deep Gated Recurrent Unit (GRU) architecture, improving detection effectiveness while reducing computational overhead. Our proposed approach also utilizes a Target Sampling strategy to balance class distributions, enhancing the model’s ability to accurately identify minority attacks. The BPSO algorithm is employed to identify the most influential features from the high-dimensional network traffic datasets, enhancing model interpretability and supporting more efficient learning. This optimized feature subset is then fed into a GRU-based DL architecture augmented with MHA, which performs sequence processing and attention-based learning for intrusion detection. The performance of the proposed model is evaluated utilizing the BoT-IoT and the CIC-IDS2017 benchmark datasets, ensuring a comprehensive assessment of anomaly detection capabilities. Extensive experimental results demonstrate the superior performance of the proposed model, achieving a recall of 98.42% and 99.76%, with F1-score of 98.94% and 99.76% for binary classification and a recall of 99.79% and 98.69%, with F1-score of 99.89% and 98.04% for multiclass classification on the BoT-IoT and CIC-IDS2017 datasets, respectively, highlighting the effectiveness of our model in enhancing threat detection for computer networks and IoT environments in comparison to recent state-of-the-art IDSs.

Enhancing IoT Network Security: A BPSO-Optimized Attention-GRU Deep Learning Framework for Intrusion Detection

Key Points

Abstract

Cite This Study