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Meta-Learner-Based Approach for Detecting Attacks on Internet of Things Networks.
Shaza Dawood Ahmed Rihan1, Mohammed Anbar2, Basim Ahmad Alabsi1
1Applied College, Najran University, King Abdulaziz Street, Najran P.O. Box 1988, Saudi Arabia.
Sensors (Basel, Switzerland)
|October 14, 2023
Summary
This study introduces a meta-learning approach for identifying Internet of Things (IoT) network attacks. The method enhances security by combining deep learning models, with XGBoost achieving 98.75% accuracy.
Area of Science:
- Cybersecurity
- Artificial Intelligence
- Network Security
Background:
- The proliferation of Internet of Things (IoT) devices expands the attack surface for cyber threats.
- High data volumes from IoT devices can overwhelm traditional security systems, hindering effective threat detection.
- Existing security measures struggle to cope with the scale and complexity of IoT network vulnerabilities.
Purpose of the Study:
- To propose a novel meta-learning framework for enhanced attack identification in IoT networks.
- To address the security challenges posed by the increasing number of interconnected IoT devices and data overload.
- To evaluate the efficacy of a meta-learner model integrating multiple deep learning and machine learning algorithms.
Main Methods:
- Developed a meta-learner by stacking predictions from Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) models.
- Employed Logistic Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) for meta-learner identification.
- Conducted extensive evaluations using the 2020 IoT dataset to assess model performance.
Main Results:
- The Extreme Gradient Boosting (XGBoost) model achieved the highest accuracy (98.75%), precision (98.30%), F1-measure (98.53%), and AUC-ROC (98.75%).
- The Support Vector Machine (SVM) model demonstrated the highest recall (98.90%), showing a marginal improvement over XGBoost.
- The meta-learning approach effectively enhanced attack detection capabilities in IoT environments.
Conclusions:
- The proposed meta-learning framework offers a robust solution for identifying attacks in complex IoT networks.
- XGBoost and SVM models show significant promise for real-time threat detection and mitigation in IoT security.
- This approach provides a scalable and effective strategy to bolster the security posture of interconnected IoT ecosystems.
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