I-MPaFS: enhancing EDoS attack detection in cloud computing through a data-driven approach

Md. Sharafat Hossain

doi:10.1186/s13677-024-00699-5

Return

I-MPaFS: enhancing EDoS attack detection in cloud computing through a data-driven approach

2024-10-28

Md. Sharafat Hossain

Md. Alamgir Hossain *

Md Saiful Islam

DOI：10.1186/s13677-024-00699-5

Original

Original request for help

Save

Abstract

En 中文

Cloud computing offers cost-effective IT solutions but is susceptible to security threats, particularly the Economic Denial of Sustainability (EDoS) attack. EDoS exploits cloud elasticity and the pay-per-use billing model, forcing users to incur unnecessary costs. This research introduces the Integrated Model Prediction and Feature Selection (I-MPaFS) framework to address EDoS attacks. I-MPaFS framework enhances an existing dataset to improve performance, using the generated data to build a Random Forest model for EDoS detection. Our investigation employs the UNSW-NB15, CSE-CIC-IDS18 and NSL-KDD datasets, demonstrating the proposed method's superiority over existing techniques. The model achieved recall scores of 99.45% on the UNSW-NB15 dataset, 98.19% on the CSE-CIC-IDS18 dataset, and 99.82% on the NSL-KDD dataset, highlighting its reliability and efficacy in safeguarding cloud users from financial exploitation. This study contributes to the field by evaluating current EDoS detection methods, introducing the I-MPaFS framework, validating its performance with benchmark datasets, and comparing its effectiveness against state-of-the-art techniques. The findings affirm the significant potential of I-MPaFS in enhancing cloud security and protecting users from EDoS attacks.

Keywords:

Economic denial of sustainability (EDoS)

Machine learning in cloud security

Financial impact of cyberattacks

EDoS detection framework

Cloud security

Cloud service economic safety