harrychien1311/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.

Jupyter Notebook