Adaboost Model-Based Approach for Effectively Detecting Spam in IoT Devices

Abstract

Abstract: The number of Internet of Things (IoT) devices in smart homes is rising swiftly, producing enormous amounts of data that are mostly transmitted over wireless communication channels. IoT devices can be at risk from a variety of threats, such as hacker attacks, cyberattacks, erratic network connectivity, data leakage, etc. By evaluating vast amounts of data using complex algorithms, machine learning may assist uncover spam in IoT data. It can also help to raise the security level of the IoT system in smart homes by utilising statistical analysis and machine learning to find anomalies in the data. In this work, two machine learning models—the Bagged model and the Adaboost model—are evaluated using a wide range of criteria employing a vast number of input feature sets. Each model generates a spam score using the improved input attributes. The suggested algorithm is used to determine the network's linked IoT devices' spamicity score. The REFIT Smart Home dataset is used to test the suggested method. The outcomes show that the suggested strategy is beneficial when compared to other current plans.