Paper #4

Scalable AI-Based Framework for Anomaly Detection and MQTT Traffic Optimisation in IoT Systems

Emran Aljarrah, Bilal Bataineh, Saleem Alzoubi, Md Nasiruzzaman and Mahdi H. Miraz

Abstract: Internet of Things (IoT) networks are becoming increasingly prevalent, but facing serious challenges with regards to data integrity and communication security, due to resource constraint IoT devices being used in large distributed networks. This paper proposes a scalable AI-based anomaly detection framework and efficient traffic optimisation for Message Queuing Telemetry Transport (MQTT) messages sent through IoT systems. Both real-time data collected from the IOT nodes as well as the CICIDS2017 dataset have been utilised for this research. Due to biases and inaccuracies inherent in the data, a Conditional Generative Adversarial Network (CGAN) model was used for pre-processing the data. A novel selective feature process has also been presented, which first performs Principal Component Analysis (PCA), Genetic Algorithm and Univariate Statistical Test, and then perform dimensionality reduction using the Extreme Learning Machine (PCA-GA-UST-ELM) model. The traffic optimisation process for the overall IoT network uses the integration of a Deep Neural Network (DNN) model and an MQTT parsing engine, to facilitate processing of network traffic through MQTT. The anomalous threats presented in the IoT nodes were classified using the Bidirectional Gated Recurrent Unit (Bi-GRU) model to improve threats detection accuracy of the proposed DNN models. Finally, the routing process of messages has been optimised using a tail hybridisation method of GTLPSO (Game Theory, Linear Programming, Stochastic Problems). The performance of the framework has been evaluated in terms of accuracy, precision, latency, Prediction time and throughput, for varying numbers of epochs and IoT nodes.

Keywords: Anomaly detection; CICIDS2017 dataset; IoT systems; MQTT; Scalable AI.