Abstract: Data processing plays a significant role in improving the performance of machine learning models. In an IoT network, data generated by sensor nodes will be multivariate and consist of complex patterns and correlations. Such data must be carefully preprocessed before applying ML algorithms for classification. In this paper, we propose a study that improving anomaly classification accuracy on various IoT sensor datasets. In this context, we propose a system that deals with various data distributions and detects anomalies more efficiently. The proposed study uses multiple data transformation methods to prepare the data for further analysis. The data transformation facilitates data conversion, making it more suitable for machine learning models. We demonstrate the efficacy of the proposed method on widely used IoT sensor datasets. We also demonstrate the results of the proposed system with the various machine learning models using performance metrics such as accuracy, AUC Score, F1 score, and Mean Square Error (MSE). It is observed that the proposed system is more effective on various IoT datasets.

Key words: anomaly, data preprocessing, data transformation, IoT, machine learning, multivariate sensor data