Classification of DDoS Attacks Using Ensemble Learning

In an era where the digital realm is indispensable to daily life, Constantly posing a threat, distributed denial of service (DDoS) attacks are capable of disrupting vital online services. Traditional methods of DDoS attack classification often fall short when facing the ever-evolving landscape of attack techniques. This paper unveils a journey through the complexities of enhancing DDoS attack classification with a focus on improving accuracy and adaptability. We embarked on this research by individually training a plethora of machine learning models. However, our preliminary findings highlighted the striking dis-parity in accuracy across different models, highlighting how complex and varied DDoS attacks are. Driven to overcome these constraints, we resorted to the sturdy framework of ensemble learning. Ensemble learning changed the game for accurate DDoS attack classification by effortlessly combining the best features of several base models. To be more precise, we blended two distinct baseline models: K-Nearest Neighbors (KNN) and Random Forest (RF). These models, each with distinct characteristics, combined to form a flexible and adaptive DDoS attack classification system. K-Nearest Neighbors (KNN) uses the proximity of data points to find trends and parallels. Resilience, high-dimensional data processing, and overfitting resistance are all favoured by the Random Forest (RF) ensemble of decision trees. Combining these several methods into a group greatly improved our categorization accuracy. Our research's ability to forecast accuracy using the ensembled model was one of its main results. This prediction accuracy indicates the flexibility and dependability of the classification model in addition to providing a crucial performance standard for assessing system effectiveness. Finally, our explo-ration of the DDoS assault classification demonstrated Ensemble Learning's superiority. The proposed strategy surpassed the existing methodologies, according to experimental findings, and recorded a remarkable accuracy of 84.10% when classifying data related to DDoS attacks. With each stride, we fortified our defenses against dynamic DDoS attacks. We anticipate that this approach will continue to evolve, safeguarding the digital world against the relentless waves of DDoS threats.