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.
Keywords: Attacks and detection; Dataset, Internet of Things, Machine Learning, Vulnerabilities.
How to cite this article: Anand TR, Priya MT, Senthilkumar PS, Vijayalakshmi PS, Thirunavukkarasu V, Kanna RR., Performance Evaluation of Machine Learning Models for Detecting Vulnerabilities in Internet of Things Network. Int J Drug Deliv Technol. 2026;16(2s): 627-638; DOI: 10.25258/ijddt.16.627-638