A novel intrusion detection system: integrating greedy sand cat swarm optimization and dual attention graph convolutional networks

M. Prabu (), L. Sasikala, S. Suresh and R. Ramya
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M. Prabu: SRM Institute of Science and Technology
L. Sasikala: SRM Institute of Science and Technology
S. Suresh: SRM Institute of Science and Technology
R. Ramya: St. Joseph’s College of Engineering

International Journal of System Assurance Engineering and Management, 2025, vol. 16, issue 11, No 2, 3562-3582

Abstract: Abstract The rise of smart devices and network vulnerabilities has led to a surge in cyber-attacks. Detecting and classifying malicious traffic is vital for system security. This paper proposes a novel framework for intrusion detection using advanced machine learning techniques to improve cybersecurity. The framework initiates comprehensive data collection from the BoT-IoT and NSL-KDD datasets, followed by rigorous data pre-processing steps including normalization, label encoding, and outlier identification. Feature extraction is performed to capture key characteristics of the data, and dimensionality minimization techniques are applied to improve computational efficiency. A feature selection process is executed using the Greedy Sand Cat Swarm Optimization algorithm that identifies the most informative features for analysis. These features are then processed by a Dual Attention Graph Convolutional Neural Network, designed to reveal complex patterns in network traffic data. The framework outperforms traditional methods with the accuracy and precision of 99.19% 99.08% respectively. Overall, these findings highlight that the classification performance of the proposed model is highly accurate, making a significant contribution to improving intrusion detection and network security.

Keywords: Cyber attacks; Smart device; Anomaly detection; Dimensionality minimization; Graph convolutional neural network; Greedy sand cat swarm optimization; Network traffic (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13198-025-02874-6

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