LUFlow: Attack Detection in the Internet of Things Using Machine Learning Approaches

The number of Internet of Things (IoT) equipment and the data those devices produce have both increased significantly over the past few years. Due to their resource limitations, IoT network participants can be problematic, hence it’s important to integrate security on these devices. Attackers now have more reasons to target IoT devices due to this. It is crucial to develop strategies to counter such attacks and shield IoT devices from malfunction. In order to accurately detect attacks and abnormalities in the IoT environment, the performances of multiple machine learning (ML) approaches have been compared in this paper. Here Support Vector Machine (SVM), K-Nearest Neighbour (KNN) and Random Forest (RF) have been applied as ML approaches utilizing the most recent LUFlow dataset. The performance measures such as accuracy, recall, precision and f1-score have been calculated for the comparison of the above-mentioned ML techniques. The system found that RF had the highest accuracy, at 97.7%, compared to SVM and KNN, which had accuracy rates of 92.80% and 94.7%, respectively.