Title: A novel method for intrusions detection in IoT enabled environment

Authors: Ravi Kumar Saidala; Surekha Y.; Lalitha Kumari Gaddala; Anjaneyulu Kunchala; Ramakrishna Reddy Mule; Ravi Kumar Tirandasu

Addresses: Department of CSE-Data Science, School of Engineering and Technology, CMR University, Bangalore, India ' Department of Computer Science and Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh 520007, India ' Department of Computer Science and Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh 520007, India ' Department of Computer Science and Engineering, Sanskriti University, Mathura, Uttar Pradesh, India ' Department of Computer Science and Engineering, Narasaraopeta Engineering College, Andhra Pradesh 522601, India ' Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, 522302, AP, India

Abstract: One of the most significant study areas in recent years has been the internet of things. It is suggested to use a supervised machine learning intrusion detection system (IDS) to identify IoT attacks with a high detection accuracy of 99.99% and an MCC of 99.97%. Using the minimum-maximum normalisation technique for feature scaling, an efficient intrusion detection system (IDS) for the internet of things (IoT) is built to prevent information leakage on the test set. Because of this, it is necessary to provide a greater contribution to this context for the internet of things environment by assessing various AI-based algorithms on datasets that are capable of properly capturing the various aspects of the environment. Not only that, but we also looked at the effects of various approaches for feature engineering, such as correlation analysis and information gain.

Keywords: internet of things; IoT; machine learning; deep learning; network security.

DOI: 10.1504/IJESDF.2026.153335

International Journal of Electronic Security and Digital Forensics, 2026 Vol.18 No.3, pp.370 - 384

Received: 23 Apr 2024
Accepted: 15 Jul 2024
Published online: 01 May 2026 *

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