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Network Forensics and Intrusion Detection in MQTT-Based Smart Homes


Lama AlNabulsi, Sireen AlGhamdi, Ghala AlMuhawis, Ghada AlSaif, Fouz AlKhaldi, Maryam AlDossary, Hussian AlAttas, and Abdullah AlMuhaideb


Vol. 23  No. 4  pp. 95-102


The emergence of Internet of Things (IoT) into our daily lives has grown rapidly. It’s been integrated to our homes, cars, and cities, increasing the intelligence of devices involved in communications. Enormous amount of data is exchanged over smart devices through the internet, which raises security concerns in regards of privacy evasion. This paper is focused on the forensics and intrusion detection on one of the most common protocols in IoT environments, especially smart home environments, which is the Message Queuing Telemetry Transport (MQTT) protocol. The paper covers general IoT infrastructure, MQTT protocol and attacks conducted on it, and multiple network forensics frameworks in smart homes. Furthermore, a machine learning model is developed and tested to detect several types of attacks in an IoT network. A forensics tool (MQTTracker) is proposed to contribute to the investigation of MQTT protocol in order to provide a safer technological future in the warmth of people’s homes. The MQTT-IOT-IDS2020 dataset is used to train the machine learning model. In addition, different attack detection algorithms are compared to ensure the suitable algorithm is chosen to perform accurate classification of attacks within MQTT traffic.


Internet of things (IoT), Message Queuing Telemetry Transport (MQTT), machine learning, anomaly detection, smart home.