Unraveling the Complexities of Application Layer DDoS Attacks - Machine Learning Implementation

The increasing prevalence of Application Layer Distributed Denial of Service (DDoS) attacks poses a significant threat to the availability and reliability of network services. This research project focuses on developing an effective DDoS attack detection system using machine learning techniques, specifically targeting application layer protocols. The study involves a comprehensive data collection process, followed by meticulous data preprocessing and feature engineering to extract discriminative features for distinguishing normal network traffic from DDoS attack patterns. Various supervised and unsupervised machine learning algorithms are implemented and evaluated to assess their performance in detecting and mitigating application layer DDoS attacks. In light of the evolving tactics used by sophisticated DDoS attackers, recent techniques face challenges in adapting to new attack methods. This paper aims to address these limitations by proposing advanced machine learning-based detection systems, emphasizing the critical need for improved DDoS detection mechanisms. Through critical analysis and thorough implementation analysis, this research work aims to contribute to the advancement of DDoS detection mechanisms, thereby enhancing network security in the face of evolving cyber threats.