A Comparative Study of Machine Learning Methods for Intrusion Detection

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Authors
Sibai, Fadi N.
Asaduzzaman, Abu
Sibai, Ahmad
Issue Date
2023-05
Type
Conference paper
Language
en-US
Keywords
Support vector machines , Knowledge engineering , Operating systems , Neural networks , Intrusion detection , Forestry , Computer crime
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Abstract

In this work, we applied 8 machine learning (ML) techniques to detect intrusions, namely, neural networks, kNN, SVM, random forest, trees, AdaBoost, naive Bayes, and stochastic gradient descent SGD. Using the NSL-KDD data set, these ML techniques were trained and tested to correctly classify the network and operating system records of this dataset into one of 24 possible attacks. The performances of these ML methods were analyzed and compared, with the random forest method performing at the top. To the best of our knowledge, this is the first work on investigating more than 4 ML classifiers on this data set in one single work and using the same set of tools.

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10th International Conference on Electrical and Electronics Engineering, ICEEE 2023, May 8, 2023 - May 10, 2023
Citation
F. N. Sibai, A. Asaduzzaman and A. Sibai, "A Comparative Study of Machine Learning Methods for Intrusion Detection," 2023 10th International Conference on Electrical and Electronics Engineering (ICEEE), Istanbul, Turkiye, 2023, pp. 184-188, doi: 10.1109/ICEEE59925.2023.00041.
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Institute of Electrical and Electronics Engineers Inc.
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