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- W4200008989 abstract "Intrusion detection systems play a vital role in protecting computer systems from external attacks. In recent years, many open-source intrusion datasets have been released to let researchers study and evaluate their detection methods. These datasets are presented with a comprehensive feature set. In this paper, we study the intrusion detection problem using a dataset CIDDS-002 that is provided with a small number of features. Our experimental using different classification algorithms including Logistic Regression, xgboost, Random Forest and catboost show that we can achieve very high predictive performance without using a lot of features generated in other datasets." @default.
- W4200008989 created "2021-12-31" @default.
- W4200008989 creator A5076385867 @default.
- W4200008989 date "2021-10-22" @default.
- W4200008989 modified "2023-09-26" @default.
- W4200008989 title "Evaluating machine learning algorithms for intrusion detection systems using the dataset CIDDS-002" @default.
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- W4200008989 doi "https://doi.org/10.1145/3494885.3494906" @default.
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