Network Intrusion Detection Packet Classification with the HIKARI-2021 Dataset: a study on ML Algorithms

Fernandes, Rui; Lopes, Nuno

doi:10.1109/isdfs55398.2022.9800807

Cited by 7 publications

(2 citation statements)

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“…Different algorithms for supervised learning were already tested in the HIKARI-2021 dataset such as gradient boosting machine (GBM), Light GBM, CatBoost, and XGBoost in the research made by Maya Louk [21], a Multi-layer Perception [22], Graph Neural Networks (GNNs) [23] and other traditional ML algorithms such as KNN, Random Forest and SVM [19]. Although some of them achieve great results and interesting findings, most of the studies mention the importance of further research in the dataset by using different models.…”

Section: Related Workmentioning

confidence: 99%

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

Silva,

Fernandes,

Lopes

2024

Preprint

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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.

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Section: Related Workmentioning

confidence: 99%

“…The combination of the Genetic Algorithm and XGBoost model will also be compared to other dimensionality reduction techniques, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and the Chi Squared-test results from a previous study [19].…”

Section: Introductionmentioning

confidence: 99%