ItemArticle

Performance Evaluation of Machine Learning Models for Detecting Vulnerabilities in Internet of Things Network

Title
Performance Evaluation of Machine Learning Models for Detecting Vulnerabilities in Internet of Things Network
Creator
Anand, T.R.; Priya, Mohana T; Senthilkumar, Poorana S; Vijayalakshmi, P.S.; Thirunavukkarasu, V.; Rajesh Kannae, R.
Description
Security threats and attacks are a growing concern in the field of Internet of Things (IoT) infrastructure. Internet-based automated network application models are used across various domains; commensurately, different security vulnerabilities and anomaly attacks are also increased at the same level. These attacks could cause failures in IoT infrastructure and network systems. In the modern world, Machine Learning (ML) models support various predictive analyses, providing more accurate results for future forecasting in various fields. In this article, we compare existing classical Machine Learning (ML) algorithms supported by Artificial Intelligence (AI) to evaluate and predict the performance and accuracy of different vulnerabilities in IoT infrastructure. We considered and compared the results of Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN) using publicly available datasets. Through this evaluation, we obtained an accuracy of 99.4% from DT, RF, and ANN. Additionally, RF demonstrated a highest accuracy of F1 is 0.994 and lowest STD variance is 0.014 than compared models in the selected dataset. 2026, Dr. Yashwant Research Labs Pvt. Ltd. All rights reserved.
Source
International Journal of Drug Delivery Technology;Volume;16;Issue;2;pp.627-638
Date
01-01-2026
Publisher
Dr. Yashwant Research Labs Pvt. Ltd.
Subject
Attacks and detection; Dataset; Internet of Things; Machine Learning; Vulnerabilities
Coverage
Anand T.R., Department of Computer Technology, Dr. N. G. P. Arts and Science College, Tamil Nadu, Coimbatore, India; Priya M.T., Department of Computer Science, CHRIST University, Karnataka, Bangalore, India; Senthilkumar P.S., Department of Computer Applications, Dr. N. G. P. Arts and Science College, Tamil Nadu, Coimbatore, India; Vijayalakshmi P.S., Department of Computer Applications, Dr. N. G. P. Arts and Science College, Tamil Nadu, Coimbatore, India; Thirunavukkarasu V., Department of Computer Science, CHRIST University, Karnataka, Bangalore, India; Rajesh Kannae R., Department of Computer Science, CHRIST University, Karnataka, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 9754415;
Format
online
Language
English
Type
Article

Citation

Anand, T.R.; Priya, Mohana T; Senthilkumar, Poorana S; Vijayalakshmi, P.S.; Thirunavukkarasu, V.; Rajesh Kannae, R., “Performance Evaluation of Machine Learning Models for Detecting Vulnerabilities in Internet of Things Network,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23427.

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