Research on Intelligent Detection and Prevention Mechanism of Malicious Traffic in the Internet of Things Based on Machine Learning

The increasing integration of Internet of Things (IoT) devices into critical infrastructure has brought unprecedented connectivity and significant information security challenges such as malicious traffic and network intrusions. This paper applies machine learning (ML) techniques to intelligently detect and prevent such malicious traffic in IoT environments. The paper explains in detail how ML techniques such as supervised learning, unsupervised learning, deep learning, and federated learning can be used to enhance the security posture of IoT by detecting malicious patterns in real time, adaptively, and accurately. Special emphasis is placed on the malicious traffic categories that are unique to IoT, the shortcomings of traditional intrusion detection systems, and how intelligent ML-based approaches can overcome these issues through autonomous feature learning and behavioral analysis. Key challenges such as data imbalance, weak generalization capabilities, real-time processing requirements, and model vulnerability to adversarial attacks are discussed in detail. The paper concludes by pointing out future research avenues for efficient, scalable, and explainable machine learning-based security solutions for IoT, making this review a comprehensive roadmap for advancing intelligent information security to address impending cyber threats.