This source code is used for the project two-phase deep learning based EDoS detection system
Cloud computing is currently considered the most cost-effective platform for offering business and consumer IT services over the Internet. However, it is prone to new vulnerabilities. A new type of attack, called an economic denial of sustainability (EDoS) attack, exploits the pay-per-use model to scale up the resource usage over time to the extent that the cloud user has to pay for the unexpected usage charge. In this project, we proposed a two-phase deep learning-based detection system to detect EDoS attack. The first phase called the prediod detector will detect where there is an attack in a period of 5s and then trigger the second phase detector if there is an attack in that 5-second period. The second detector called the flow detector will detect abnormal flows in the abnormal period detected by the first detector.
- Create and activate a virtual conda environment:
conda create -n edos python=3.11
conda activate edos
- Install dependencies:
./setup.sh
./two_phase_EDoS_detector.sh
- Please read
two_phase_EDOS.pdf
to understand the solution and read theprocess-steps.pptx
to understand more about techical installation
If you use this source code, please cite this publication:
"Nhu, C.-N.; Park, M. Two-Phase Deep Learning-Based EDoS Detection System. Appl. Sci. 2021, 11, 10249. https://doi.org/10.3390/app112110249"