A comprehensive machine learning framework for robust security management in cloud-based internet of things systems - Mahmoud Mohamed,; Khaled Alosman, (Penerbit Universiti Kebangsaan Malaysia, 2024)
The purpose of this paper is to explore the role of Machine Learning (ML) in fortifying the security of cloud-based Internet of Things (IoT) systems, using a comprehensive security management approach. The methodological approach involved comparing different ML techniques such as Decision Trees, Random Forest, Support Vector Machines, and Convolutional Neural Networks. Their effectiveness was evaluated based on the accuracy of threat detection in cloud-based IoT systems. The findings revealed that Convolutional Neural Networks demonstrated the highest accuracy rate (98%) in threat detection, thereby significantly enhancing the security of IoT systems. It also identified improvements in threat detection, prevention, response, and system recovery across all ML techniques. Research limitations were primarily the rapidly evolving nature of both ML and IoT technologies, necessitating continual reassessments. The scope was also limited to cloud-based IoT systems, leaving room for further research on other types of IoT systems. The practical implications included improved system security, which could lead to increased trust and wider adoption of IoT technology in various sectors, from healthcare to home security. The social implications entail a safer digital environment, contributing to data privacy and reducing the risk of cyber threats for individuals and communities. The originality of this paper lies in its comprehensive approach to IoT security management using ML, providing valuable insights into the effectiveness of different ML techniques in enhancing threat detection accuracy.