Enhancing IoT Security through Ensemble Classification Models and Image Processing Techniques

Abstract

The explosive growth of IoT devices in a wide range of industries is the starting point for many vulnerabilities, making networks weaker. Multiple IDS solutions are present nowadays, and their ability to handle complications and threats could be enhanced. This study suggests a new approach which demonstrates the possibilities of converting the network traffic data into RGB images using image processing techniques and provides the algorithms of the machine learning applications with additional strength using ensembles such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF). The approach rests on the individual capabilities of each classifier. Subsequently, they are integrated into a weighted-voting scheme to ensure the detection precision for any malicious activity inside the IoT environment. This proposed model is more proficient than the traditional methods in arresting a wide range of illegal entries because the process delivers high precision and authenticity rates. This success corroborates that our methodology is an excellent basis for building a comprehensive image processing and ensemble learning platform to enhance the security of IoT devices.