Publication Date

Spring 2024

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Genya Ishigaki

Second Advisor

Fabio Di Troia

Third Advisor

Sayma Akther

Keywords

Network Intrusion Detection, Distributed Denial of Service, Ensemble Learning, Meta-Learner, Mininet, Ryu Controller.

Abstract

In response to the security threats posed by Distributed Denial of Service (DDoS) attacks, this paper presents an intrusion detection framework with a high-accuracy multi-class classification model. In addition to detecting the existence of DDoS attacks, our framework aims to identify the type of attack (e.g., protocol or message type) so that the system can select the most appropriate countermeasure against the DDoS type. We leverage a meta-learner to build an ensemble model of multiple machine learning models such as LSTM, RF, and KNN to enhance detection and classification accuracy. Tested on the CIC-DDoS 2019 dataset, the proposed model achieves 96% accuracy in the type identification task, while a simple combination of existing detection classifiers only achieves 92% accuracy in the same type identification. To demonstrate a practical implementation of the DDoS classification model, we integrated the model with the Ryu SDN controller running on a Mininet network testbed, which emulates different types of DDoS attacks and benign traffic. The integrated ensemble model achieved 93% accuracy in identifying the DDoS types in the testbed experiment.

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Available for download on Sunday, May 25, 2025

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