Authors : Tarun Badiwal; Suresh Chand Meena; Sandeep Jayswal; Utkarsh Sahai Saxena

Volume/Issue : Volume 11 - 2026, Issue 7 - July

Google Scholar : https://tinyurl.com/54dpf9af

Scribd : https://tinyurl.com/2ycn2ap9

DOI : https://doi.org/10.38124/ijisrt/26jul1164

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The rapid growth of the Internet of Things (IoT) has enabled seamless communication among billions of interconnected devices. However, the heterogeneous and resource-constrained nature of IoT networks makes them vulnerable to cyberattacks such as unauthorized access, spoofing, malware injection, and denial-of-service attacks. This research proposes an Object Identifier Detection System (OIDS) to strengthen IoT network security by uniquely identifying and authenticating connected devices based on object identifiers and behavioral characteristics. This paper presents a novel Object Identifier Detection System (OIDS) to enhance security in Internet of Things (IoT) networks. The proposed framework authenticates IoT devices using unique object identifiers and continuously monitors network traffic to detect unauthorized devices and malicious activities. It integrates machine learning-based anomaly detection with object identifier verification to improve attack detection accuracy while reducing false positives. Experimental evaluation demonstrates that the proposed system provides secure, scalable, and efficient protection for IoT environments compared with conventional intrusion detection approaches.

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