Detection and Analysis of DDoS Attack Using a Collaborative Network Monitoring Stack

In recent years, Distributed Denial of Service (DDoS) attacks have become common and have evolved into a standard method of targeting entities. These attacks can be used to make money or to damage the network and systems of an organisation. The majority of recent research in this field has focused on implementing various machine learning (ML) strategies to detect and thwart these attacks. Even though these techniques have a high rate of accuracy, they occasionally struggle to detect zero-day vulnerabilities or manage intense network traffic. This investigation shifts its focus from DDoS detection to the creation of a cost-effective network monitoring system. The intended solution will provide real-time visibility into a simulated network environment on Graphical Network Simulator-3 (GNS3) and incorporate a machine learning (ML) engine for enhanced DDoS detection via network monitoring tools such as Prometheus, Zabbix, and Grafana. This initiative seeks to provide organisations with a comprehensive, cost-effective solution that extends beyond DDoS mitigation by emphasising a more cost-effective and efficient monitoring system. During the implementation of the project, a few technical obstacles prevented the integration of certain components into the solution, as will be described in the paper. However, an integrated solution and collaborative design will increase the solution's ability to detect more sophisticated DDoS attacks.