aspg-20-2384-1791304756 20261006163916 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Wireless and Ad Hoc Communication IJWAC 2692-4056 2023 7 2 Mahmoud A. Zaher Faculty of Artificial Intelligence, Data Science department, Egyptian Russian University (ERU), Cairo, Egypt Yahia B. Hassan Electrical Eng. Dept, Higher Institute of Engineering, Minia, Egypt Nabil M. Eldakhly Faculty of Computers and Information, Sadat Academy for Management Sciences, Cairo, Egypt & French University in Cairo, Egypt The proliferation of botnet threats within Internet of Things (IoT) networks has underscored the critical need for robust detection mechanisms. This study addresses this imperative by presenting a comprehensive framework employing Machine Learning (ML) techniques for botnet detection. Leveraging a dataset sourced from authentically compromised IoT devices, the research delves into the intricate behaviors exhibited by botnets, emphasizing the encounters pretended by their polymorphic characteristics. A convolutional neural network architecture, featuring stacked layers with residual connections, serves as the cornerstone of the proposed detection system. The efficiency of the developed model is evaluated using meticulous visualization of data insights, learning behaviors, and detection performance, which demonstrate a great ability to discriminate between different botnet activities. This study presents a prominent improvement to the cybersecurity field by developing an effective solution for invigorating IoT network defenses against developing botnet threats, which highlights the essential role of ML-driven methods in the preservation of the integrity of interconnected devices. 2023 56 63 2384 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJWAC.070204 https://americaspg.com/journal/20/article/2384 https://americaspg.com/storage/91704494645.pdf