Version 1
: Received: 2 May 2024 / Approved: 3 May 2024 / Online: 3 May 2024 (09:39:07 CEST)
How to cite:
Silva, J. L.; Fernandes, R.; Lopes, N. Performance Study on the Use of Genetic Algorithm for Reducing Feature Dimensionality on an Embedded Intrusion Detection System. Preprints2024, 2024050182. https://doi.org/10.20944/preprints202405.0182.v1
Silva, J. L.; Fernandes, R.; Lopes, N. Performance Study on the Use of Genetic Algorithm for Reducing Feature Dimensionality on an Embedded Intrusion Detection System. Preprints 2024, 2024050182. https://doi.org/10.20944/preprints202405.0182.v1
Silva, J. L.; Fernandes, R.; Lopes, N. Performance Study on the Use of Genetic Algorithm for Reducing Feature Dimensionality on an Embedded Intrusion Detection System. Preprints2024, 2024050182. https://doi.org/10.20944/preprints202405.0182.v1
APA Style
Silva, J. L., Fernandes, R., & Lopes, N. (2024). Performance Study on the Use of Genetic Algorithm for Reducing Feature Dimensionality on an Embedded Intrusion Detection System. Preprints. https://doi.org/10.20944/preprints202405.0182.v1
Chicago/Turabian Style
Silva, J. L., Rui Fernandes and Nuno Lopes. 2024 "Performance Study on the Use of Genetic Algorithm for Reducing Feature Dimensionality on an Embedded Intrusion Detection System" Preprints. https://doi.org/10.20944/preprints202405.0182.v1
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 in a low-capacity network. The results, comparing the GA with other well-known techniques in feature selection and reduction, show that the GA has a higher F1-score of 76%, although the time to find the optimal set of features surpasses other methods; the reduction of the number of features reduces the processing time without a significant impact in f1-score. The Jetson Nano allows the classification of network traffic with an overhead of 10 times in comparison to a traditional server.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
