Optimized intrusion detection in IoT and fog computing using ensemble learning and advanced feature selection

The proliferation of Internet of Things (IoT) devices and fog computing architectures has introduced major security and cyber threats. Intrusion detection systems have become effective in monitoring network traffic and activities to identify anomalies that are indicative of attacks. However, constra...

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Bibliographic Details
Published in:PloS one 2024-08, Vol.19 (8), p.e0304082
Main Author: Tawfik, Mohammed
Format: Article
Language:English
Subjects:
Access control
Accuracy
Algorithms
Anomalies
Benchmarks
Biology and Life Sciences
Cloud Computing
Collaboration
Communications traffic
Computer and Information Sciences
Computer Security
Cybersecurity
Cyberterrorism
Data encryption
Deep learning
Detectors
Earth Sciences
Edge computing
Engineering and Technology
Ensemble learning
Feature selection
Fog
Infrastructure
Internet of Things
Intrusion detection systems
Machine Learning
Neural Networks, Computer
Nodes
Optimization techniques
Physical Sciences
Traffic
Traffic analysis
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Summary:The proliferation of Internet of Things (IoT) devices and fog computing architectures has introduced major security and cyber threats. Intrusion detection systems have become effective in monitoring network traffic and activities to identify anomalies that are indicative of attacks. However, constraints such as limited computing resources at fog nodes render conventional intrusion detection techniques impractical. This paper proposes a novel framework that integrates stacked autoencoders, CatBoost, and an optimised transformer-CNN-LSTM ensemble tailored for intrusion detection in fog and IoT networks. Autoencoders extract robust features from high-dimensional traffic data while reducing the dimensionality of the efficiency at fog nodes. CatBoost refines features through predictive selection. The ensemble model combines self-attention, convolutions, and recurrence for comprehensive traffic analysis in the cloud. Evaluations of the NSL-KDD, UNSW-NB15, and AWID benchmarks demonstrate an accuracy of over 99% in detecting threats across traditional, hybrid enterprises and wireless environments. Integrated edge preprocessing and cloud-based ensemble learning pipelines enable efficient and accurate anomaly detection. The results highlight the viability of securing real-world fog and the IoT infrastructure against continuously evolving cyber-attacks.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0304082