Intrusion Detection and Analysis in IoT Devices Using Machine Learning Models

View Sample PDF

Author(s): Ankit Kumar Jain (National Institute of Technology, Kurukshetra, India), Pooja Kumari (National Institute of Technology, Kurukshetra, India)and Ritesh Gupta (National Institute of Technology, Kurukshetra, India)
Copyright: 2024
Pages: 26
Source title: Challenges in Large Language Model Development and AI Ethics
Source Author(s)/Editor(s): Brij Gupta (Asia University, Taichung City, Taiwan)
DOI: 10.4018/979-8-3693-3860-5.ch012

Keywords: Artificial Intelligence / Data Modeling Languages / Engineering Science Reference / Science & Engineering

Purchase

View Intrusion Detection and Analysis in IoT Devices Using Machine Learning Models on the publisher's website for pricing and purchasing information.

Abstract

This study proposes utilizing deep learning and machine learning techniques to identify network anomalies. The IoT-23 dataset serves as the basis for the analysis. The proposed approach models are designed to classify network flows as benign or assign them to one of the 11 labels in the dataset, as well as to differentiate between malicious and benign connections. Performance and time costs of various models are compared to determine the optimal algorithm for maximum performance in minimal time. This comparison identifies the better performing model with the least overhead cost for deployment on IoT devices, ensuring the security and privacy of users by blocking malicious connections. The experimental results show that decision tree offers maximum efficiency and the lowest overhead cost, making it suitable for use in IoT devices.

The IRMA Community

Research IRM

Intrusion Detection and Analysis in IoT Devices Using Machine Learning Models

Purchase

Abstract

Related Content

IRMA Sponsors