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@article{183380,
        author = {Varun Slathia},
        title = {IOT Device Identification using Lightweight Feature by Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {2070-2075},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183380},
        abstract = {As the Internet of Things (IoT) sector expands, it becomes paramount to identify and protect these devices from insecurities that could otherwise undermine cybersecurity and operational integrity. Conventional identification methods often depend on complex features that are, Usually very resource hungry, which is impractical for constrained IoT environments. They are based on the lightweight features that provide basic and yet very meaningful device attributes and use machine learning to facilitate IoT device identification effectively. We analyze different machine learning algorithms on a dataset with the variety of IoT devices and show that lightweight features have the potential of reaching good performance in terms of correctness while maintaining a light computational footprint. The approaches we present in our findings are effective and scalable for IoT security frameworks. 
A multitude of lightweight features is used in identification techniques based on machine learning, including Decision Trees, Random Forests, and Support Vector Machines (SVM).},
        keywords = {},
        month = {August},
        }