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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 5, 2024.
Abstract: Intrusion detection systems (IDS) play a critical role in safeguarding network security by identifying malicious activities within network traffic. However, the effectiveness of an IDS hinges on its ability to extract relevant features from the vast amount of data it collects. This study investigates the impact of different feature extraction methods on the performance of IDS. We compare the performance of various feature extraction techniques on two widely used intrusion detection datasets: KDD Cup 99 and NSL-KDD. By evaluating these techniques on both datasets, we aim to gain insights into the generalizability and robustness of each method across different dataset characteristics. The study compares the performance of these methods using standard metrics like detection rate, F-measure and FPR for intrusion detection.
CHENTOUFI Oumaima, CHOUKHAIRI Mouad, CHOUGDALI Khalid and ALLOUG Ilyas, “Exploring the Impact of PCA Variants on Intrusion Detection System Performance” International Journal of Advanced Computer Science and Applications(IJACSA), 15(5), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150539
@article{Oumaima2024,
title = {Exploring the Impact of PCA Variants on Intrusion Detection System Performance},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150539},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150539},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {5},
author = {CHENTOUFI Oumaima and CHOUKHAIRI Mouad and CHOUGDALI Khalid and ALLOUG Ilyas}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.