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- W4285817375 abstract "With tremendous data volume generated by various types of devices, the Internet faces a serious challenge in network security. As one of the primary countermeasures, IDS (Intrusion Detection System) has been widely used for the identification of suspicious flows; however, it lacks enough flexibility, adaptability and generalization capability for new attacks. Recently, ML (Machine Learning) has been widely discussed for application in IDS, and a number of works have proven its superiority in traffic classification, though with different sorts of algorithms. Thus, to further check their performance, in this paper, we perform extensive simulations with several representative ML-based intrusion detection algorithms evaluated through the KDD dataset, including LR (Logistic Regression), DT (Decision Tree), RF (Random Forest), NB (Naive Bayes), SVM (Support Vector Machine), and DNN (Deep Neural Network), where key indicators contain Precision, Recall, Accuracy, F1-Score and Training time. The corresponding results show that RF is more efficient for its relatively high detection accuracy with low complexity. In addition, we think ensemble learning and deep learning have the potential to perform better in detecting attacks." @default.
- W4285817375 created "2022-07-19" @default.
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- W4285817375 date "2022-02-01" @default.
- W4285817375 modified "2023-09-23" @default.
- W4285817375 title "Performance Comparisons of Machine-Learning-Based Intrusion Detection Algorithms through KDD Dataset" @default.
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- W4285817375 doi "https://doi.org/10.1109/acctcs53867.2022.00010" @default.
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