Enhancing Security with Machine Learning for IoT Intrusion Detection Systems

Registration ID: IJNRD_305045

Published ID: IJNRD2504033

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Gaurav Mehta

Machine learning Internet of things Security Intrusion detection Classification algorithm.

The Internet of Things (IoT) has seen rapid growth, connecting millions of devices across various sectors, from healthcare to smart homes. However, this expansion has introduced significant security vulnerabilities, making IoT systems prime targets for cyberattacks. Traditional security mechanisms often struggle to cope with the dynamic nature and scale of IoT networks, necessitating more sophisticated and adaptive solutions. This paper presents an improved security framework for IoT environments through the integration of Machine Learning (ML) techniques for Intrusion Detection Systems (IDS). By leveraging supervised and unsupervised learning models, the proposed approach enhances the detection accuracy and response times to potential threats, while adapting to evolving attack patterns. The system analyzes network traffic, device behavior, and system logs to identify abnormal activities and potential intrusions in real-time. Key ML algorithms, such as Decision Trees, Random Forest, and Neural Networks, are evaluated for their effectiveness in distinguishing between normal and malicious activities. The results demonstrate that the ML-based IDS significantly outperforms traditional signature-based methods, offering higher detection rates and reduced false positive rates. This work contributes to the advancement of IoT security by providing a scalable, adaptive, and efficient solution to safeguard IoT ecosystems against emerging threats.

Published Paper   E-Certificate

Gaurav Mehta (April-2025). Enhancing Security with Machine Learning for IoT Intrusion Detection Systems. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(4), a281-a288. https://ijnrd.org/papers/IJNRD2504033.pdf

Volume 10 Issue 4, April-2025

Pages : a281-a288

Paper Reg. ID: IJNRD_305045

Published Paper Id: IJNRD2504033

Downloads: 000122002

Research Area: Science and Technology

Author Type: Indian Author

Country: Ambala, Haryana, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2504033.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2504033

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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Call For Paper - Volume 10 | Issue 11 | November 2025

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