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Manoranjini, John
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Electrical & Electronics Engineering

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Hybrid deep learning ensemble with score-based feature optimization for cyber attack detection in IoT systems Manoranjini, John; Gaddam, Venugopal; Raghavender, Kotla Venkata; Battu, Hanumantha Rao; Sunitha, Pamarthi; Shanmugam, Sathish Kumar
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11674

Abstract

The rapid growth of internet of things (IoT) devices have improved connectivity but also exposed networks to cyber threats. This study proposes a prediction-scoring-based ensemble deep learning model with prediction-scoring-optimized feature selection (EDLM-PSOFS) for intrusion detection in IoT systems. The model integrates random forest (RF) feature extraction with ant lion optimization (ALO)-tuned convolutional neural networks (CNNs) to balance accuracy and computational efficiency. Using the KDD Cup ’99 dataset containing 4.9 million traffic records and 41 features, the framework achieved 97% accuracy, 0.99 precision, and 0.97 recall within five epochs. Comparative evaluation shows faster convergence and reduced complexity than gated recurrent units (GRU), long short-term memory (LSTM), and support vector machine (SVM) baselines, demonstrating suitability for real-time, resource-constrained IoT deployments.
Co-Authors Battu, Hanumantha Rao Gaddam, Venugopal Raghavender, Kotla Venkata Shanmugam, Sathish Kumar Sunitha, Pamarthi