Journal of Sustainable Development and Green Technology JSDGT 10.54216/JSDGT https://www.americaspg.com/journals/show/2045 2022 2022 Faculty of Information Systems and Computer Science, October 6th University, Cairo, Egypt Ayman H. Abdel Abdel-aziem Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Sharqiyah, Egypt Tamer H. M. Soliman As the Internet of Things (IoT) continues to expand, the security of connected devices becomes a paramount concern. Malicious actors exploit vulnerabilities in these devices, leading to severe consequences such as data breaches, privacy infringements, and service disruptions. Traditional security measures struggle to keep pace with the evolving threat landscape, necessitating advanced solutions. In this paper, we present a pioneering approach to fortify the security of IoT environments against malware through the integration of advanced machine intelligence techniques. Our work addresses this critical concern by introducing a comprehensive Machine Intelligence Strategy designed to detect and classify malware in IoT ecosystem. Leveraging Support Vector Machines (SVM) with different kernel choices, our strategy offers a multi-faceted defense mechanism. Through extensive experimentation and evaluation on public dataset of malware images, we demonstrate the efficacy of our strategy in fortifying the guardianship of connected devices, fostering a safer and more resilient IoT ecosystem. Beyond technical contributions, our research fosters a deeper understanding of the symbiotic relationship between machine intelligence and IoT security, propelling advancements in safeguarding the ever-expanding landscape of interconnected devices. 2023 2023 44 52 10.54216/JSDGT.020205 https://www.americaspg.com/articleinfo/38/show/2045