TRUST-BASED MODEL FOR SECURE ROUTING AGAINST RPL ATTACKS IN INTERNET OF THINGS USING MACHINE LEARNING ALGORITHMS
Author(s):
R. Elango, Dr.D.Maruthanayagam
Keywords:
Trust-Based Routing, IoT Security, RPL Attacks, Machine Learning, Random Forest, Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN) and Neural Networks.
Abstract
The Internet of Things (IoT) is increasingly susceptible to security threats, particularly targeting the Routing Protocol for Low-Power and Lossy Networks (RPL). Ensuring secure and reliable routing is crucial for the performance and trustworthiness of IoT networks. This paper proposes a trust-based model for secure routing against RPL attacks by leveraging machine learning algorithms, including Random Forest, Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), and Neural Networks. The model calculates node reputation and detects anomalies to prevent routing attacks. The performance of the proposed model is evaluated using key metrics: Node Reputation, Anomaly Detection Metrics, Routing Overhead, Energy Efficiency, Throughput, and Packet Loss Rate. Experimental results demonstrate the effectiveness of the proposed model in enhancing IoT network security and efficiency while maintaining low overhead.
Article Details
Unique Paper ID: 166510

Publication Volume & Issue: Volume 11, Issue 2

Page(s): 817 - 839
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