AN EFFICIENT SPAM DETECTION TECHNIQUE FOR IOT DEVICES USING MACHINE LEARNING

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. 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 the security of the IoT devices by detecting spam using machine learning. To achieve 
this objective, Spam Detection in IoT using Machine Learning framework is proposed. 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. REFIT Smart Home dataset is used for the validation of proposed technique. The 
results obtained proves the effectiveness of the proposed scheme in comparison to the other existing schemes.