Designing a Robust Machine Learning-Based Framework for Secure Data Transmission in Internet of Things (IoT) Environments: A Multifaceted Approach to Security Challenges

This research develops a machine learning framework for protecting data as it is transmitted in Internet of Things (IoT) configurations. The main objective of the proposed framework to address the major security issues using two intelligent machine learning methods are Random Forest and Support Vector Machine (SVM). They are applied to detect strange behaviour and potential threats within IoT data. The system was evaluated based on accuracy, precision, recall, and F1-score to determine how successful it was. Performance indicated Random Forest performed very well with 93.5% accuracy, slightly higher than SVM 91.2%. The system was also quite good at detecting cyber-attacks such as DDoS and malware, and did not raise many false alerts. This indicates that the system can actually contribute to making IoT much safer, building on what we have in this field. This study implies that incorporating machine learning into IoT security can assist in developing improved defenses against emerging cyber-attacks. In the long term, this research can assist in subsequent studies in order to improve security systems for various uses of IoT, address existing problems, and utilize more data.