Deep Learning for Anomaly Detection in IoT Systems: Techniques, Applications, and Future Directions

Abstract

The proliferation of Internet of Things (IoT) devices has revolutionized various sectors by enabling real-time data collection and processing, leading to unprecedented levels of efficiency and convenience. However, this increased connectivity and data flow also introduce significant security risks, particularly in the realm of anomaly detection. This study delves into the use of deep learning techniques for identifying abnormalities in IoT systems, providing a thorough analysis of state-of-the-art methods, assessing their effectiveness across different IoT scenarios, and exploring future research directions. We review current deep learning-based anomaly detection techniques, examine their applications in real-world settings, and discuss potential improvements and innovations. By synthesizing the latest research and developments, this paper aims to offer a comprehensive understanding of how deep learning can bolster the security and performance of IoT systems. Our findings highlight the importance of robust anomaly detection mechanisms in safeguarding IoT networks and underscore the need for continued advancements in this area. Through this study, we seek to contribute valuable insights into the application of deep learning for enhancing IoT system security and reliability, ultimately supporting the sustainable growth and integration of IoT technologies across various domains.