Performance Study on the Use of Genetic Algorithm for Reducing Feature Dimensionality in an Embedded Intrusion Detection System

Abstract

Intrusion Detection Systems play a crucial role in a network. They can detect different network attacks and raise warnings on them. Machine Learning-based IDSs are trained on datasets that, due to the context, are inherently large, since they can contain network traffic from different time periods and often include a large number of features. In this paper, we present two contributions: the study of the importance of Feature Selection when using an IDS dataset, while striking a balance between performance and the number of features; and the study of the feasibility of using a low-capacity device, the Nvidia Jetson Nano, to implement an IDS. The results, comparing the GA with other well-known techniques in Feature Selection and Dimensionality Reduction, show that the GA has the best F1-score of 76%, among all feature/dimension sizes. Although the processing time to find the optimal set of features surpasses other methods, we observed that the reduction in the number of features decreases the GA processing time without a significant impact on the F1-score. The Jetson Nano allows the classification of network traffic with an overhead of 10 times in comparison to a traditional server, paving the way to a near real-time GA-based embedded IDS.