Advancements in IoT Anomaly Detection: Leveraging Machine Learning for Enhanced Security
Rajesh Rajaan, Baldev Singh, Nilam Choudhary · Advances in intelligent systems research/Advances in Intelligent Systems Research · 2025
The internet of things(IoT) is one of the fastest-growing technologies that has disrupted industries by allowing devices to connect without disruption.Nevertheless, the adoption of IoT devices has posed new security risks as it is hard to distinguish between normal and anomalous behaviors indicative of cyber-attacks or system issues.This paper aims at discussing the present progress of IoT anomaly detection; this has the argument that most of the solution relies on machine learning (ML).ML has been shown to be very influential in this field thanks to its ability to unravel sophisticated patterns from large, diverse datasets, something which many conventional approaches are incapable of.This paper investigates various approaches used in anomaly detection in IoT using ensemble learning, deep learning, and a combination of both.An insight into their strengths and weaknesses is given.Important directions for further research are the use of ensemble learning methods that combine several classifiers for better detection ability and deep learning models such as CNN and LSTM for temporal and spatial data analysis.A discussion also ensues to show how to overcome class imbalance in IoT datasets using SMOTE method.Also, it highlights that there is a demand for immediate dynamic anomaly detection for IoT systems since the nature of IoT is unsteady and emphasizes the utilization of the explainable AI (XAI) to enhance the users' trust in models.We also find gaps in the literature: first, insufficient exploration of the use of ensemble methods; second, high demand for comparing different approaches; third, the necessity to develop low-power models for IoT devices.This is an area that should receive further attention when it comes to prospective work because creating better, more flexible, and finer weight structures to protect IoT network appearances against new threats should be the goal of future work.Based on the findings in this paper, there is need for continual research in the IoT anomaly detection where such concerns as performance, flexibility and interpretability need to be met to establish secure IoT systems.