Abstract
Smart homes are gradually incorporating Internet of Things (IoT) devices, producing massive amounts of data. Wireless methods are used to convey the vast majority of this data. Several IoT devices may be impacted by a variety of IoT risks, including cyberattacks, inconsistent network connectivity, data leakage, etc. Machine learning (ML) techniques could be quite helpful in this case to guarantee security and authorization. Data anomalies must be discovered using statistics. This study investigates the dependability of IoT equipment that interacts. When determining a spam score, the six Machine Learning models are taken into account with increased input features including xgboost, BGLM, GLM, Bagging, and Stacking. The grade demonstrates the dependability of an IoT device based on many criteria. This grade demonstrates the dependability of an IoT device based on many criteria. When compared to other recent approaches, the findings demonstrate the effectiveness of the proposed strategy. The spamicity score of the home IoT device is calculated using the Spam Score algorithm. A publicly available dataset for smart homes meteorological data from the UCI repository and synthetic data from IoT devices are used to validate the technique. The results collected show just how effective the suggested algorithm is in analyzing time series data from the Internet of Things devices for spam identification.
Keywords: Classification, Feature Engineering, Internet of Things, Machine Learning, Regression, Spam Detection.