Date of Award

12-2024

Degree Name

MS in Computer Science

Department/Program

Computer Science

College

College of Engineering

Advisor

Franz Kurfess

Advisor Department

Computer Science

Advisor College

College of Engineering

Abstract

As the integration of artificial intelligence (AI) within cybersecurity continues to

grow, machine learning (ML) and deep learning (DL) models are increasingly used to

detect cyber attacks. However, these models are rarely evaluated in real-time attack

scenarios to see how subtle changes from the real networking environment can affect

their predictions. To address this issue, we propose a scalable, platform-independent

Docker testbed specifically designed for simulating real-time Distributed Denial of

Service (DDoS) attack scenarios that allows researchers to deploy and evaluate their

pre-trained, ML and DL detection models. Our framework is simple to configure

and can run across Intel and ARM CPUs, as well as Windows, Linux, and MacOS

operating systems. The testbed was validated with our six pre-trained models in

a 10-minute DDoS attack simulation, where performance metrics such as resource

consumption were actively monitored across different operating systems and CPUs.

This Dockerized environment offers researchers an accessible and flexible solution for

testing and improving DDoS detection models in a realistic, real-time context.

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