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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/1725
DC FieldValueLanguage
dc.contributor.authorNguyen Ba Leen_US
dc.date.accessioned2023-10-26T04:46:21Z-
dc.date.available2023-10-26T04:46:21Z-
dc.date.issued2023-
dc.identifier.urihttps://epub.vgu.edu.vn/handle/dlibvgu/1725-
dc.description.abstractDistributed Denial of Service, also called DDoS for short, is well recognized as a prevalent form of cyber attack. Numerous approaches and solutions have been devised to address DDoS attacks. The utilization of data mining methodologies and algorithms has facilitated researchers in extracting valuable information and identifying patterns from extensive datasets, hence enhancing the precision and efficiency of classification tasks. Our goal in this thesis is to leverage various machine learning algorithms for classifying network traffic data into two categories: ”Benign” or ”DDoS”. As well as evaluating the results of these algorithms based on time cost, accuracy, F1, recall, and precision score in order to find the best overall performing algorithm. This paper encompasses a comprehensive depiction of the CICIDS2017 dataset, which serves as the dataset for the data mining task. Additionally, we will give a discussion of the data pre-processing, and feature selection techniques employed in this study, along with a comprehensive overview of DDoS attacks. We utilize Random Forest, Naive Bayes (Multinomial and Gaussian), Logistic Regression, and Multi-Layer Perceptron (MLP) algorithms implemented in Python on the Google Colaboratory platform for this study.en_US
dc.language.isoenen_US
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectDistributed denial of serviceen_US
dc.subjectData miningen_US
dc.subjectRandom forresten_US
dc.subjectNaive bayesen_US
dc.subjectLogistic regressionen_US
dc.subjectMulti-Layer perceptronen_US
dc.titleDDoS attacks detection with the use of data mining algorithmsen_US
dc.typeThesisen_US
item.languageiso639-1other-
item.fulltextWith Fulltext-
item.grantfulltextrestricted-
Appears in Collections:Computer Science (CS)
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