Vulnerabilities and limitations of MQTT protocol used between IoT devices

With the proliferation of smart devices capable of communicating over a network using different protocols, each year more and more successful attacks are recorded against these, underlining the necessity of developing and implementing mechanisms to protect against such attacks. This paper will review some existing solutions used to secure a communication channel, such as Transport Layer Security or symmetric encryption, as well as provide a novel approach to achieving confidentiality and integrity of messages. The method, called Value-to-Keyed-Hash Message Authentication Code (Value-to-HMAC) mapping, uses signatures to send messages, instead of encryption, by implementing a Keyed-Hash Message Authentication Code generation algorithm. Although robust solutions exist that can be used to secure the communication between devices, this paper considers that not every Internet of Things (IoT) device or network design is able to afford the overhead and drop in performance, or even support such protocols. Therefore, the Value-to-HMAC method was designed to maximize performance while ensuring the messages are only readable by the intended node. The experimental procedure demonstrates how the method will achieve better performance than a symmetric-key encryption algorithm, while ensuring the confidentiality and integrity of information through the use of one mechanism

Machine learning based IoT Intrusion Detection System:an MQTT case study (MQTT-IoT-IDS2020 Dataset)

The Internet of Things (IoT) is one of the main research fields in the Cybersecurity domain. This is due to (a) the increased dependency on automated device, and (b) the inadequacy of general-purpose Intrusion Detection Systems (IDS) to be deployed for special purpose networks usage. Numerous lightweight protocols are being proposed for IoT devices communication usage. One of the distinguishable IoT machine-to-machine communication protocols is Message Queuing Telemetry Transport (MQTT) protocol. However, as per the authors best knowledge, there are no available IDS datasets that include MQTT benign or attack instances and thus, no IDS experimental results available. In this paper, the effectiveness of six Machine Learning (ML) techniques to detect MQTT-based attacks is evaluated. Three abstraction levels of features are assessed, namely, packet-based, unidirectional flow, and bidirectional flow features. An MQTT simulated dataset is generated and used for the training and evaluation processes. The dataset is released with an open access licence to help the research community further analyse the accompanied challenges. The experimental results demonstrated the adequacy of the proposed ML models to suit MQTT-based networks IDS requirements. Moreover, the results emphasise on the importance of using flow-based features to discriminate MQTT-based attacks from benign traffic, while packet-based features are sufficient for traditional networking attacks