Version 1
: Received: 27 December 2023 / Approved: 27 December 2023 / Online: 27 December 2023 (08:34:17 CET)
How to cite:
Prasetya, I.K.; Ahsan, M.; Mashuri, M.; Lee, M.H. Multivariate Robust MRCD Based Hotelling’s T2 Control Chart with Bootstrap Control Limit for Intrusion Detection. Preprints2023, 2023122062. https://doi.org/10.20944/preprints202312.2062.v1
Prasetya, I.K.; Ahsan, M.; Mashuri, M.; Lee, M.H. Multivariate Robust MRCD Based Hotelling’s T2 Control Chart with Bootstrap Control Limit for Intrusion Detection. Preprints 2023, 2023122062. https://doi.org/10.20944/preprints202312.2062.v1
Prasetya, I.K.; Ahsan, M.; Mashuri, M.; Lee, M.H. Multivariate Robust MRCD Based Hotelling’s T2 Control Chart with Bootstrap Control Limit for Intrusion Detection. Preprints2023, 2023122062. https://doi.org/10.20944/preprints202312.2062.v1
APA Style
Prasetya, I.K., Ahsan, M., Mashuri, M., & Lee, M.H. (2023). Multivariate Robust MRCD Based Hotelling’s T2 Control Chart with Bootstrap Control Limit for Intrusion Detection. Preprints. https://doi.org/10.20944/preprints202312.2062.v1
Chicago/Turabian Style
Prasetya, I.K., Muhammad Mashuri and Muhammad Hisyam Lee. 2023 "Multivariate Robust MRCD Based Hotelling’s T2 Control Chart with Bootstrap Control Limit for Intrusion Detection" Preprints. https://doi.org/10.20944/preprints202312.2062.v1
Abstract
Intrusion detection is generally carried out by matching network traffic patterns with known attack patterns or by identifying abnormal network traffic patterns. One statistical methodological approach used in intrusion detection is Statistical Process Control (SPC) by constructing a control chart. Hotelling’s T2 control chart is a multivariate control chart commonly used to monitor the mean process. The performance of the T2 chart in monitoring mean shifts can be increased if a robust estimator is utilized. Based on previous research, T2 based on the Fast-MCD estimator has good performance in monitoring low to medium outlier contaminated data. Therefore, the MRCD estimator can be used to detect intrusion. On the other hand, this research focuses on developing a bootstrap-based robust Hotelling’s T2 charts with Fast-MCD and MRCD estimators for evaluating performance in detecting intrusion on intrusion detection datasets. Based application of UNSW-NB15, the proposed chart has better performance than the conventional T2 and Fast-MCD-based T2 despite the longer execution time.
Keywords
bootstrap; intrusion detection; multivariate control chart; MRCD; Hotelling’s T2
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
