Enhanced Anomaly Detection in IoT: A Transformer Based Approach for Multivariate Time Series data

In IoT (Internet of Things) systems, the identification of anomalies plays a crucial role in ensuring the security and reliability of data. The rapid progression of digitization has led to the widespread deployment of sensor networks across various domains, supplying vital information for organizations to achieve complete autonomy. These sensor networks generate copious amounts of data, often in the form of multivariate time series, capturing both typical operational patterns and unusual occurrences. In the realm of anomaly detection for multivariate time series within IoT networks, our research introduces a transformer-based approach. The chosen model employs a transformer encoder-decoder architecture, leveraging its robust capabilities to analyze and identify aberrant patterns within intricately connected and complex data streams. To enhance the accuracy of anomaly detection, the study addresses the challenges posed by multivariate time series data and proposes a distinctive transformer-based methodology. Through experimentation and evaluation of authentic IoT dataset, this research illustrates the potential of the proposed model in delivering improved anomaly detection performance, consequently contributing to the development of more resilient and efficient IoT systems.