Enhancing DDoS Detection in Cloud Computing Environment Through Effective Feature Selection With SMOTE

The growing reliance on internet-based services and the increasing sophistication of cyber threats have made network security a crucial concern in modern day computing. These attacks can disrupt operations, result in financial losses, damage reputations, and undermine trust in digital services. Distributed denial of service (DDoS) attacks has emerged as a critical challenge for cloud computing, impacting service availability and raising concerns among providers. Despite cloud computing's scalable and flexible architecture, its vulnerabilities make it an attractive target for attackers. This paper presents a comprehensive survey of DDoS attacks in cloud environments, focusing on detection mechanisms leveraging Synthetic Minority Oversampling Technique (SMOTE). The paper focuses on the analysis of cloud computing characteristics exploited by attackers, and a discussion of effective anomaly detection approaches. Solutions based on SMOTE, encompassing detection parameters, metrics and features were reviewed for their ability to enhance security with high accuracy and low computational costs. The results present 39 different feature selection as depicted in table 2. It recommends that different feature selection and resampling techniques be studied toward developing a faster system for identifying imbalance data for DDoS attack detection.