Integrated Machine Learning Approach for Attack Detection in MQTT-Enabled Smart Home Systems

With the worldwide rising demand for smart home technologies and home automation, which ensures better privacy and security by using IoT devices in these environments has become more important. This research paper discusses and explores the MQTT protocol, which is an efficient and lightweight messaging protocol commonly used in IoT systems for communication. To identify and mitigate cyber-attacks in smart home systems, this study applies a set of machine learning algorithms to analyze MQTT attack dataset, which aims to detect and classify various cyber-attacks using multiple machine learning approaches with high accuracy and combine their prediction to find out the final result. The study begins with an introduction of smart home, its components, basic introduction, architecture of MQTT protocol, and major attack vectors targeting MQTT-based systems, highlighting vulnerabilities that can be exploited by attackers. Afterward this research paper proposes and implementing a model which uses a set of machine learning algorithms on MQTT attack dataset to train and test models, these become capable to identify anomalies in MQTT traffic, which enables the detection of attacks such as dos attack, flooding packet attack, SlowITe Attack and brute force attack for unauthorized access of servers or systems. Feature selection and data standardization done, all trained and tested models demonstrate high accuracy and effectiveness in threat detection and classification on a comprehensive MQTT dataset. This study illustrates the effectiveness and good application of AI/ML-driven approaches for security leak prevention which is boosting the smart home environments security attack detection and contributing towards the secure IoT ecosystems.