• Mr. Venkateswar Lal P K
• P. Chenchuramanuja

with the proliferation of Internet of Things (IoT) devices, securing IoT networks against malicious activities,particularly Bot-IoT traffic, has become a critical concern. This paper presents a novel approach for detecting malicious Bot-IoT traffic in IoT networks using machine learning techniques. The proposed method involves collecting a comprehensive dataset of network traffic, encompassing both normal and malicious traffic samples. Relevant features are extracted from the traffic data, including packet characteristics, IP addresses, ports, and temporal patterns. Supervised, unsupervised, or semi-supervised machine learning algorithms are then employed to classify network traffic into benign or malicious categories. The model is trained on labeled data, evaluated using standard performance metrics, and deployed within the IoT network infrastructure for real-time monitoring.

A Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine Learning Techniques

"A Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.209-215, June-2024, Available :http://www.jetir.org/papers/JETIRGL06036.pdf

"A Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp209-215, June-2024, Available at : http://www.jetir.org/papers/JETIRGL06036.pdf