A hybrid deep learning approach for BoT-IoT intrusion detection

Abstract

Internet of things (IoT) devices enhance quality of life and industrial operations but pose significant security risks, necessitating intelligent intrusion detection systems (IDS) to combat evolving cyber threats. This paper proposes a novel IDS framework integrating bio-inspired heuristic feature selection, a generative adversarial network (GAN)-based data augmentation, and an ensemble classifier combining ResNet, AlexNet, and MobileNet. The methodology, tested on the botnet (BoT)-IoT dataset, follows four stages: preprocessing, feature augmentation, feature selection, and ensemble classification. Evaluated on benchmarks including CIC-IDS-2018, NSL-KDD, and UNSW-NB15, the model achieved accuracies of 98.2%, 99.1%, 97.6%, and 98.4%, respectively, with consistently high precision, recall, and F1-scores, demonstrating robust detection of diverse cyberattacks. Beyond accuracy, the framework optimizes processing time for large-scale IoT data, addressing scalability challenges in real-time threat mitigation. By synergizing feature optimization, synthetic data generation, and deep learning architectures, the solution enhances detection rates while minimizing computational overhead. Comparative analysis highlights its superior performance over existing methods, positioning it as a vital tool for securing IoT ecosystems against unauthorized access and malicious activities. The results underscore its potential to fortify IoT network security, balancing efficiency, adaptability, and computational feasibility for practical deployment in resource-constrained environments.