Debashish Roy

Work place: Humber College, Faculty of Applied Science and Technology, Toronto, Canada

E-mail: debashish.roy@humber.ca

Website: https://orcid.org/0000-0002-1174-3868

Research Interests:

Biography

Debashish Roy is a multifaceted expert at the intersection of Machine Learning (ML), Computer Network Security (CNS), and Education. With a distinguished academic background encompassing a Ph.D. and M.Sc. in ML and CNS, he possesses a comprehensive understanding of both the theoretical foundations and practical applications within these fields. Over the past two decades, he has honed his expertise not only through rigorous academic study but also through extensive teaching experience, guiding students through the complexities of these disciplines and fostering a passion for innovation. Beyond the classroom, he has demonstrated remarkable versatility and leadership in software development, serving as both a team leader and research project lead. Currently, he serves as a Professor at Humber College Institute of Technology & Advanced Learning, Toronto, ON, Canada. His research interests include network security, Internet of Things (IoT), data mining, and machine learning.

Author Articles
Classifying IoT Device’s Traffic Traces Using Network Traffic Characteristics

By Rajarshi Roy Chowdhury Debashish Roy Pg Emeroylariffion Abas

DOI: https://doi.org/10.5815/ijieeb.2025.03.01, Pub. Date: 8 Jun. 2025

The escalating proliferation of devices, including both Internet of Things (IoT) and non-IoT devices, has triggered a suite of emergent security challenges in cyberspace, such as accurate device identification and authentication. The wide array of device types, protocols, and usability exacerbates these challenges. While conventional addressing schemes such as the logical Internet Protocol addressing and physical Media Access Control addressing schemes are integral for communication, they are susceptible to spoofing attacks. Device fingerprinting can be used to address the issue of identifying devices and traffic types using only implicit identifiers such as network traffic characteristics. In this paper, supervised machine learning based a device fingerprinting model has been proposed for the classification of both IoT and non-IoT devices on three levels based on their communication traffic characteristics. A meticulous feature selection process, employing two attribute evaluators, identified a subset of twenty features crucial for generating unique fingerprints from a large set of features pool. Three publicly available datasets and two supervised classifiers were utilized for evaluation purposes. Experimental results illustrated that the proposed model attained a classification accuracy exceeding 99% in discerning between known and unknown traffic traces (Level-1) on both the UNSW IoT and D-Link IoT datasets using the Random Forest (RF) classifier, and 99.74% accuracy in classifying network traffic types (Level-2) on the UNSW dataset. Individual device identification (Level-3) proves equally robust, with the RF and J48 classifiers achieving 99.03% and 98.14% accuracies on the UNSW non-IoT and IoT datasets, respectively. These findings underscore the potential of the device fingerprinting model in enhancing network security. The model’s robust classification capabilities across various datasets and identification levels make it a valuable asset in tackling modern security challenges in networked environments.

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