Security Protocols Using Artificial Intelligence to Prevent Internet of Things Attacks

Authors

  • Mukesh Rajput Assistant Professor, School of Engineering and Computer, Dev Bhoomi Uttarakhand University, Uttarakhand, India,
  • Feon Jaison Assistant Professor, Department of Computer Science and IT, Jain(Deemed-to-be University), Bangalore-27, India
  • Aaditya Jain Assistant Professor, College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
  • Rajeev Mathur Professor, School of Engineering & Technology, Jaipur National University, Jaipur, India

Keywords:

Internet of Things (IoT), anomaly detection, security risks, Bird Swarm-Optimized artificial neural networks (BSO-ANN)

Abstract

Intelligent towns and cities, medical care, and automation in industries have all reaped countless benefits from the Internet of Things (IoT) devices quick proliferation. Although anomaly detection is essential for safeguarding the integrity and dependability of IoT systems, the widespread implementation of connected devices also brings new security problems. Bird Swarm-Optimized artificial neural networks (BSO-ANN) are a unique anomaly detection framework presented in this study for IoT contexts. The BSO approach enables the model to look for an ideal network configuration that improves anomaly detection accuracy by mimicking the collective actions of birds in a swarm. The BSO-ANN approach's ability to detect anomalies was assessed using the UNSW-NB15 dataset. The findings show that the BSO-ANN algorithm detects several types of irregularities in IoT systems with impressive precision, recall, accuracy, and f1-measure parameters. This work can act as a basis for creating sophisticated anomaly detection methods to defend IoT networks against new security risks.

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Published

11.07.2023

How to Cite

Rajput, M. ., Jaison, F. ., Jain, A. ., & Mathur, R. . (2023). Security Protocols Using Artificial Intelligence to Prevent Internet of Things Attacks. International Journal of Intelligent Systems and Applications in Engineering, 11(8s), 129–134. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3031

Issue

Vol. 11 No. 8s (2023): Advances on Machine Learning and Artificial Intelligence in Computer Technology

Section

Research Article

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