• Venkumahanti Ashok Kumar
• Bone Gayatri Neelima Sannihita
• Tampara Rambabu
• Kanda Rushikesh
• Krishna Vardhan Nadiminti

In this study, we explore two predictive modeling approaches—Decision Tree and Logistic Regression—to detect botnet attacks in IoT environments. The rapid growth of Internet-of-Things (IoT) devices has significantly expanded potential attack surfaces, making these networks increasingly vulnerable to cyber threats. Between 2017 and 2018, IoT malware attacks surged by 215.7%, rising from 10.3 million to 32.7 million, emphasizing the urgent need for effective detection and mitigation strategies. Machine learning (ML) has emerged as a promising solution to address these security challenges. we propose an optimized ML-based framework that integrates the Decision Tree (DT) classification model with the Bayesian Optimization Gaussian Process (BO-GP) algorithm to enhance the detection of IoT-based attacks. The framework is evaluated using the Bot-IoT-2018 dataset, and experimental results demonstrate its high detection accuracy, precision, recall, and F-score. These findings highlight the effectiveness of the proposed approach in securing IoT systems against the growing threat of cyberattacks, providing a robust and efficient solution for enhanced cybersecurity in IoT environments.

Botnet attacks, Machine learning, Internet of Things (IOT), Decision Tree algorithm, Bayesian Optimization Gaussian process (BO-GP)

"An Optimized Machine Learning Model for Botnet Detection in IoT Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.m529-m534, April-2025, Available :http://www.jetir.org/papers/JETIR2504C72.pdf

"An Optimized Machine Learning Model for Botnet Detection in IoT Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppm529-m534, April-2025, Available at : http://www.jetir.org/papers/JETIR2504C72.pdf