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 channels for data transmission. IoT has grown rapidly over the past decade with more than 25 billion devices are expected to be connected by 2020. The volume of data released from these devices will increase many-fold in the years to come. In addition to an increased volume, the IoT devices produces a large amount of data with a number of different modalities having varying data quality defined by its speed in terms of time and position dependency. 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 paper, we propose an innovative approach for spam detection in IoT devices using machine learning. Our technique harnesses the power of advanced machine learning algorithms to accurately identify and mitigate spam attacks, ensuring the integrity and security of IoT ecosystems. We present a comprehensive methodology that combines data collection, feature extraction, model training, and evaluation to build a robust spam detection system.