An Efficient Spam Detection Technique for IOT Devices using Machine Learning

Abstract

Internet of Things (IoT) devices is becoming increasingly popular, and as their usage grows, so does the risk of spam and malicious attacks on these devices. Spam detection is one of the primary concerns for securing IoT devices, and machine learning has shown great potential in addressing this issue. In this abstract, we will discuss the use of machine learning algorithms for spam detection in IoT devices. The proposed solution involves collecting data from IoT devices, such as sensor readings, network traffic, and user behavior, and then training a machine learning model on this data. The model can be trained on both known and unknown spam patterns, allowing it to identify and classify potential spam messages with a high level of accuracy. Several machine learning algorithms can be used for spam detection, including decision trees, support vector machines (SVMs), and neural networks. These algorithms can be trained on different features of the data, such as the frequency of certain keywords or the source of the message, to identify patterns that are indicative of spam. To ensure the effectiveness of the spam detection system, it is essential to continuously update the machine learning model as new types of spam are discovered. This can be achieved by deploying the model in the cloud, allowing it to learn from new data and improve its accuracy over time. Using machine learning algorithms for spam detection in IoT devices can greatly enhance the security of these devices. By training a model on data collected from IoT devices, we can accurately detect and classify potential spam, allowing us to take appropriate action to protect these devices from malicious attacks.