Analyzing Performance of Data Preprocessing Techniques on CPUs vs. GPUs with and Without the MapReduce Environment

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.