Countering DDoS threats: leveraging ensemble methods for detection and mitigation
Muhammad Zeeshan Arshad · Lahore Garrison University Research Journal of Computer Science and Information Technology · 2024
Cloud computing is the modern concept of distributing numerous services through the Internet, such as web applications, databases, and individuals that operate on several servers. As cloud computing technologies advance, there is a growing risk of cyberattacks that can lead to service interruptions when storing and transmitting data. The most common sort of attacks against Cloud settings is distributed denial-of-service (DDoS). Several approaches for detecting and mitigating these attacks have been offered, they are ineffective because they still fail to fully protect the system when a newer type of attacking strategy is used against the systems. In this research, we propose a method for detecting and mitigating DDoS attacks in their early phases, considering top-layer advances at the application layer and the TCP handshake mechanism. This study employs a variety of ensemble-based machine learning approaches to classify incoming data as legitimate or malicious to respond to DDoSattacks at the application layer. Furthermore, the double TCP connection concept is used to prevent DDoS. Experiments show that the stacked voting system detects DDOS attacks with the best F-score of 99.9%.