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Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | DDoSGrid-Mining: Analyzing and Classifying DDoS Attack Traffic |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2021 |
| Abstract Text | In order to provide a solution that aids in analyzing and detecting ever-increasing Distributed Denial of Service (DDoS) attacks in post, DDoSGrid was created, which off ers a research-oriented and extensible platform that provides visualizations for analyzed traffic logs and lets a user confi gure dashboards using these visualizations. However, this analysis and attack-type detection was until now performed manually, by a user looking at visualizations and metrics and making an informed decision on what attack vectors could have been used in an attack. This thesis expands on the base-idea of DDoSGrid and extends it by conceptualizing and implementing an extension that allows machine learning based attack-type classifi cation of the uploaded data sets. In addition to the already existing feature extractors that DDoSGrid provides, a new extractor is created that creates time-window based features of the traffic log. These logs can be manually classifi ed, and then added to a machine learning model, to create a true data set. This model is then used to automatically classify new data sets using different classifi cation algorithms, in order to get an attack-type analysis of the traffic log. The solution was evaluated using well-established techniques and proved to be quite eff ective, both in terms of performance and accuracy, performing comparably or in some cases better than the existing literature. |
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