Real-Time Anomaly Detection in IoT Networks Using Hybrid AI Models

Abstract

In the era of the Internet of Things (IoT), where interconnected devices generate vast amounts of data, ensuring the security and integrity of IoT networks is paramount. Anomaly detection plays a crucial role in identifying and mitigating potential threats in real-time. This research paper explores the application of hybrid artificial intelligence (AI) models for real-time anomaly detection in IoT networks. Leveraging the strengths of multiple AI techniques, including machine learning and deep learning, this approach aims to enhance the accuracy and efficiency of anomaly detection systems. Through a comprehensive review of existing literature, methodologies, and case studies, this paper elucidates the potential of hybrid AI models in bolstering the security of IoT networks against evolving cyber threats.