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Wormhole attack detection and mitigation model for Internet of Things and WSN using machine learning.
1Department of Computer Science, College of Computer Engineering and Science, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia.
Peerj. Computer Science
|September 24, 2024
Summary
This study introduces machine learning (ML) for detecting wormhole attacks in wireless sensor networks (WSNs). Support vector machine (SVM) and deep neural network (DNN) models effectively identify malicious nodes, enhancing IoT security.
Area of Science:
- Computer Science
- Cybersecurity
- Network Engineering
Background:
- The Internet of Things (IoT) expansion introduces significant security vulnerabilities, especially in wireless sensor networks (WSNs).
- WSNs face threats like wormhole attacks due to resource-constrained sensor nodes.
- Effective detection of these attacks is crucial for secure smart infrastructure.
Purpose of the Study:
- To propose and evaluate machine learning (ML) techniques for detecting wormhole attacks in WSNs.
- To analyze network node connectivity for identifying malicious activities.
- To enhance the security and reliability of IoT networks.
Main Methods:
- Utilized support vector machine (SVM) and deep neural network (DNN) models at the base station.
- Analyzed network traffic data for classification and malicious node identification.
- Validated models using NS3.37 simulator and real-world scenarios.
Main Results:
- The proposed ML models demonstrated high efficacy in detecting wormhole attacks.
- Performance was evaluated using metrics including recall, false positive rates, latency, and throughput.
- The developed approach showed superior performance compared to existing methods.
Conclusions:
- Machine learning offers a robust solution for wormhole attack detection in WSNs.
- The study highlights the potential of SVM and DNN for securing IoT environments.
- The findings contribute to improving the overall security and efficiency of connected infrastructures.
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