A data-driven approach for intrusion and anomaly detection using automated machine learning for the Internet of Things

Cyber-attacks and network intrusion have surfaced as major concerns for modern days applications of the Internet of Things (IoT). The existing intrusion detection and prevention techniques have a wide range of limitations and thus are unable to precisely detect any type of attack or anomaly within the network traffic. Many machine learning-based algorithms have also been presented by the researchers, which lack performance in terms of classification accuracy, or in terms of multi-class classification. This research presents a data-driven approach for intrusion and anomaly detection, where the data is processed and filtered using different algorithms. The quality of the training dataset is improved by using Synthetic Minority Oversampling Technique (SMOTE) algorithm and mutual information. Automated machine learning is also used to detect the algorithm with auto-tuned hyper-parameters that best suit to classify the data. This technique not only saves the computational cost to test the data at run-time but also provides an optimal algorithm without the need to run calculations to tune hyper-parameters, manually. The resultant algorithm solves a multi-class classification problem with an accuracy of 99.7%, outperforming the existing algorithms by a decent margin.