DDOS detection using ensemble techniques in machine learning models with a tailored companion tool for cyber security analysts

Abstract

With the increasing occurrence and complexity of cyber-threats, there is a need today to make use of highly performant and robust detection tools for advanced cyber-attacks such as Distributed Denial of Service (DDoS) attacks in the rapidly changing cyber-space-centric environment. In this paper, we propose a new ensemble approach that combines different machine learning models to improve the detection and classification of different DDoS attacks. We experimentally evaluate the proposed ensemble models for DDoS detection on a large and diverse but also imbalanced dataset of DDoS attack instances drawn from the CIC-DDoS2019. In this work, we analyze the influence of different ensemble voting classifiers to see the effects on the performance of the final models. Results show that 99.81% accurate detection class and 93.92% accurate classification class can be determined by our ensemble approach for identifying and classifying DDoS attacks which reflect the effectiveness and robustness of our method. We used evaluation metrics like validation accuracy and confusion matrices for the results revealed that our model is very interesting to detect cybersecurity problems such as DDoS. We are also writing a web app to plug in these performant detection, and recognition algorithms. By utilizing this platform, cyber security professionals around the world can have monitoring of their system logs and even be empowered to take necessary preemptive steps, thus creating an environment of heightened awareness and readiness for overall cyber security. In this paper, we are not only able to achieve a high detection and classification rate in DDoS attack detection and recognition by the proposed ensemble-based model, but also construct the premise of network security in academic research and network application especially when it comes to log file analysis.