MACHINE LEARNING BASED HYBRID ALGORITHM AND DDOS ATTACK PREDICTION WITH HYBRID DATASET

In this digitalized world, users of various software systems would like to securely making use of at every stage from data generation to analysis. However, blocking these services by malicious people is also an undesirable phenomenon of our world. Distributed Denial of Service (DDoS) attacks are one of the attack types. Since DDoS attack detection is important due to its increasing prevalence, this paper presents a machine learning based hybrid approach for DDoS detection. This study was performed on the popular CICIDS2017 and CIC-DDoS2019 datasets used in DDoS attack detection and an alternative hybrid dataset created by combining these datasets. This study initially employed machine learning techniques such as Decision Trees (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM) on the specified datasets, thereafter conducting a comprehensive assessment of each model's efficacy. We further evaluated the datasets employing hybrid modelling techniques that integrate several machine learning methods to enhance 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 precision 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.