Research Paper: A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT

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In this thesis, we proposed a Artificial Neural Network (ANN) for intrusion detection in the MQTT-based protocol and also compared its performance with other traditional machine learning (ML) algorithms, such as a Deep Neural Network (DNN), Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbour (kNN), Decision Tree (DT), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). The performance is proved using publicly available datasets, including MQTT-IoTIDS2020. The MQTT-IoT-IDS2020 contains three abstract-level features, including Uni-Flow, Bi-Flow, and Packet-Flow. The results for the first dataset and binary classification show that the ANN-based model achieved 99.79%, 99.85%, and 94.36% accuracies for Uni-flow, Bi-flow, and Packet-flow, respectively. However, in the case of multi-label classification, these accuracies reduced to 99.71%, 99.70%, and 92.86%, respectively.