Real-Time Security Threat Detection in IoT Devices Using Machine Learning Algorithms

Abstract

As the Internet of Things (IoT) continues to grow, ensuring the security of IoT devices has become a critical concern. Traditional security approaches are often insufficient to protect the vast and diverse ecosystem of IoT devices. This article provides a comprehensive study on the use of machine learning algorithms for enhancing security in IoT devices. We propose a novel security algorithm that leverages machine learning to detect and mitigate security threats in real-time. The algorithm utilizes a multi-layer perceptron model trained on a diverse dataset of IoT device behaviors. Through extensive experimentation and evaluation, our proposed model achieves an accuracy of 92%, outperforming other standard algorithms. The model demonstrates high precision, recall, and F1 score, indicating its effectiveness in accurately identifying security threats while minimizing false positives and false negatives. Additionally, the model exhibits low false positive and false negative rates, ensuring the robustness of the system. The training and testing performance of the model showcases its ability to adapt to different scenarios and generalize well to unseen data. Furthermore, the model maintains consistent accuracy, precision, and recall on independent validation datasets, validating its reliability and effectiveness. The proposed algorithm provides a strong foundation for enhancing security in IoT devices, addressing the unique challenges and requirements of the IoT ecosystem. This study's results add to the increasing body of IoT security research and can be used as guidelines when and implementing machine learning-based security solutions for IoT devices.