DEEP LEARNING APPROACHES FOR ANOMALY DETECTION IN IOT NETWORK ENVIRONMENTS

Abstract

With the proliferation of Internet of Things (IoT) gadgets across numerous domain names, the necessity for effective anomaly detection (AD) has end up paramount to make sure the safety and reliability of these interconnected structures. This survey focuses on deep studying (DL) methods for anomaly detection inside IoT network environments, highlighting their efficacy in identifying deviations from regular conduct. We begin with the aid of defining anomalies in the context of IoT, observed by way of an exploration of diverse DL methodologies employed for AD, which include supervised, unsupervised, and semi-supervised learning techniques. The survey categorizes present literature based on the character of IoT applications and the specific demanding situations encountered, inclusive of scalability, real-time processing, and the heterogeneity of information assets. Furthermore, we discover essential research gaps, which include the want for adaptive models that may analyze from evolving records styles and progressed interpretability of DL models. By synthesizing contemporary traits and challenges, this paper objectives to provide a comprehensive review of deep studying techniques for anomaly detection in IoT networks, guiding destiny studies directions in this rapidly advancing subject.