Enhancing IoT Anomaly Detection with DBSCAN—A Data-Driven Approach

This paper sheds light on the increasing security concerns in the domain of IoT devices and their pivotal role in the current technological landscape. Increasing dependence on these systems also increases the concerns related to potential security threats and the issues that can be caused due to malfunctioning IoT environments. This solution aims to develop a robust and efficient method for detecting anomalies and discrepancies resulting from faulty sensors and components. Insights obtained from this data are crucial for understanding the contextual conditions in which these IoT environments operate. However, the absence of checks and pre-analysis to ensure data integrity before deriving insights poses a risk to system reliability, potentially leading to erroneous decisions and actions, making it important to implement data validation measures to ensure that the insights derived from the data are reliable and correct. This is achieved with the help of various intelligent algorithms but the main focus of this paper is the employment of the DBSCAN to scrutinize datasets, detect anomalies, and extract valuable insights which can help in making the anomaly detection process more secure and easily accessible to everyone. This will help ensure that users are aware of their IoT environments.