An Efficient Spam Detection Technique for IoT Devices Using Machine Learning

Abstract

Abstract: The Internet of Things (IoT) is a group of millions of devices having sensors and actuators linked over wired or wireless channel for data transmission. The volume of data released from these devices will increase many-fold in the years to come. In such an environment, machine learning algorithms can play an important role in ensuring security and authorization based on biotechnology, anomalous detection to improve the usability and security of IoT systems. On the other hand, attackers often view learning algorithms to exploit the vulnerabilities in smart IoT-based systems. Motivated from these, in this project, we propose the security of the IoT devices by detecting spam using machine learning. In this framework, five machine learning models are evaluated using various metrics with a large collection of inputs features sets. Each model computes a spam score by considering the refined input features. This score depicts the trustworthiness of IoT device under various parameters. The results obtained proves the effectiveness of the proposed scheme in comparison to the other existing schemes.