AN EFFECTIVE SPAM DETECTION TECHNIQUES FOR IOT DEVICES USING MACHINE LEARNING

Abstract

The rapid growth of the Internet of Things (IoT) has enabled billions of interconnected devices to exchange data and provide intelligent services across various domains, including healthcare, smart homes, industrial automation, and transportation. However, the increasing deployment of IoT devices has also introduced significant security challenges, particularly in the form of spam attacks, malicious communications, and unauthorized network activities. Traditional spam detection mechanisms are often inadequate for resource-constrained IoT environments due to their limited processing capabilities and evolving attack patterns. This paper presents an effective spam detection technique for IoT devices using machine learning algorithms. The proposed framework analyzes network traffic patterns, communication behaviors, device interactions, and message characteristics to identify spam activities and malicious transmissions. Machine learning models are employed to classify legitimate and spam communications based on extracted features and behavioral indicators. Data preprocessing, feature selection, and model optimization techniques are incorporated to improve detection accuracy and computational efficiency. Experimental analysis demonstrates that the proposed approach effectively detects spam activities, reduces false positive rates, and enhances the security of IoT networks. The developed framework provides an intelligent, scalable, and efficient solution for protecting IoT ecosystems from spam-related cyber threats.