Random Forest-Based Classification of IoT Devices Using Network Traffic Analysis for Enhanced Security
DOI:
https://doi.org/10.5281/ijurd.v2i4.96Keywords:
Internet of Things, Device Classification, Random Forest, Network Traffic Analysis, Machine Learning, Security, Device FingerprintingAbstract
The rapid proliferation of Internet of Things (IoT) devices has introduced significant security challenges, particularly in device identification and access control within heterogeneous network environments. This paper presents a machine learning-based approach for IoT device classification using network traffic analysis. We employ the Random Forest algorithm, a robust ensemble learning method, to classify ten distinct IoT device categories based on their network behavioral patterns. The proposed methodology extracts seventeen discriminative features from network traffic data, including packet size distributions, protocol usage ratios, flow characteristics, and payload entropy metrics. Our experimental evaluation demonstrates that the Random Forest classifier achieves a classification accuracy of 96.6% on the test dataset, outperforming traditional machine learning approaches including Decision Trees, Naive Bayes, K-Nearest Neighbors, and Support Vector Machines. The feature importance analysis reveals that bytes per second, average packet size, and SSL/TLS ratio are the most significant discriminators for device identification. This work contributes to the growing body of research on IoT security by providing an efficient, lightweight, and scalable solution for automated device fingerprinting, enabling network administrators to enforce granular security policies and detect unauthorized device deployments in smart home and industrial IoT environments.References
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Copyright (c) 2026 Sanjeev Kumar, Sukhvinder Deora
This work is licensed under a Creative Commons Attribution 4.0 International License.
