WSEAS Transactions on Information Science and Applications
Print ISSN: 1790-0832, E-ISSN: 2224-3402
Volume 21, 2024
Cybersecurity Enhancement in IoT Wireless Sensor Networks using Machine Learning
Authors: , ,
Abstract: In the context of the Internet of Things, this paper presents approaches in order to enhancing the security in Wireless Sensor Networks. It addresses the challenges arising from the lack of standardization in IoT. On the other hand, this paper proposes a machine learning and AI algorithms to detect the intrusion detection. WSNs, which are crucial for data collection across various applications, face several security threats like eavesdropping and Denial of Service (DoS) attacks. The proposed approach in this paper present accuracy rates of 0.98 for Random Forest, 0.90 for SVM, and 0.95 for KNN. It demonstrates the effectiveness of machine learning in identifying various types of attacks. This method not only improves authentication efficiency but also significantly enhances the detection and classification of diverse security threats, paving the way for substantial advancements in cybersecurity within IoT environments.
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Keywords: Wireless Sensor Networks, IoT, Security Enhancement, SVM, KNN, Random Forest, Attack Detection, DDOS attack
Pages: 480-487
DOI: 10.37394/23209.2024.21.43