Silva, J.L.; Fernandes, R.; Lopes, N. Performance Study on the Use of Genetic Algorithm for Reducing Feature Dimensionality in an Embedded Intrusion Detection System. Systems2024, 12, 243.
Silva, J.L.; Fernandes, R.; Lopes, N. Performance Study on the Use of Genetic Algorithm for Reducing Feature Dimensionality in an Embedded Intrusion Detection System. Systems 2024, 12, 243.
Silva, J.L.; Fernandes, R.; Lopes, N. Performance Study on the Use of Genetic Algorithm for Reducing Feature Dimensionality in an Embedded Intrusion Detection System. Systems2024, 12, 243.
Silva, J.L.; Fernandes, R.; Lopes, N. Performance Study on the Use of Genetic Algorithm for Reducing Feature Dimensionality in an Embedded Intrusion Detection System. Systems 2024, 12, 243.
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:
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