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Journal article
Analyzing Performance of Data Preprocessing Techniques on CPUs vs. GPUs with and Without the MapReduce Environment
Electronics (Basel), Vol.14(18), p.3597
09/10/2025
Web of Science ID: WOS:001581373500001
Abstract
Data preprocessing is usually necessary before running most machine learning classifiers. This work compares three different preprocessing techniques, minimal preprocessing, Principal Components Analysis (PCA), and Linear Discriminant Analysis (LDA). The efficiency of these three preprocessing techniques is measured using the Support Vector Machine (SVM) classifier. Efficiency is measured in terms of statistical metrics such as accuracy, precision, recall, the F-1 measure, and AUROC. The preprocessing times and the classifier run times are also compared using the three differently preprocessed datasets. Finally, a comparison of performance timings on CPUs vs. GPUs with and without the MapReduce environment is performed. Two newly created Zeek Connection Log datasets, collected using the Security Onion 2 network security monitor and labeled using the MITRE ATT&CK framework, UWF-ZeekData22 and UWF-ZeekDataFall22, are used for this work. Results from this work show that binomial LDA, on average, performs the best in terms of statistical measures as well as timings using GPUs or MapReduce GPUs.
Details
- Title
- Analyzing Performance of Data Preprocessing Techniques on CPUs vs. GPUs with and Without the MapReduce Environment
- Publication Details
- Electronics (Basel), Vol.14(18), p.3597
- Resource Type
- Journal article
- Publisher
- MDPI; BASEL
- Number of pages
- 26
- Grant note
- US National Center for Academic Excellence in Cybersecurity (NCAE): 2021 NCAE-C-002 US National Center for Academic Excellence in Cybersecurity: H98230-21-1-0170 Cyber Research Innovation Grant ProgramAskew Institute at the University of West Florida, USA
The research was funded by the US National Center for Academic Excellence in Cybersecurity (NCAE), 2021 NCAE-C-002: Cyber Research Innovation Grant Program, Grant Number: H98230-21-1-0170. This research was also partially supported by the Askew Institute at the University of West Florida, USA.
- Copyright
- © the authors
- Identifiers
- WOS:001581373500001; 99381500018906600
- Academic Unit
- Mathematics and Statistics; Computer Science; Hal Marcus College of Science and Engineering
- Language
- English