Machine Learning Based Hybrid DDoS Attack Prediction

In this digitalized world, users of various software systems would like to securely make use of it at every stage from data generation to analysis. However, blocking these services by malicious people is also an undesirable phenomenon in our world. Since Distributed Denial of Service (DDoS) attack detection is important due to its increasing prevalence, this paper presents machine learning and hybrid approaches for DDoS detection. This study was performed on the popular CICIDS2017 and CIC-DDoS2019 datasets used in DDoS attack detection. Also, an alternative hybrid dataset is created by combining these two datasets. This study initially employed Decision Trees (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM) machine learning algorithms on the specified datasets, thereafter conducting a comprehensive assessment of each model's efficacy. We further evaluated the datasets employing hybrid modeling that integrates two machine learning methods to enhance performance, accuracy, and dependability by leveraging their respective strengths. The investigation demonstrated that hybrid models may get an accuracy of up to 99.91% on complex data sets. In our research, we combined two important datasets to construct an alternative to those utilized in existing literature. The hybrid application of machine learning methods markedly enhanced DDoS detection accuracy and optimized performance on complex datasets relative to hybrid versions of established approaches. Moreover, our results aim to improve the efficiency and flexibility of cybersecurity detection techniques and to create a foundation for future research.