AN ADAPTIVE MACHINE LEARNING FRAMEWORK FOR REAL-TIME WEB APPLICATION ATTACK AND ANOMALY DETECTION

Abstract

The project attempts to detect and prevent growing application-layer DDoS attacks, giving insights into attack patterns and tools for increased cybersecurity measures.  The project concentrates on attacks at the HTTP level, and will dissect the techniques and tools to create a special approach to help understand and defend against the emerging cyber threats. The project needs to be oriented towards accessibility of tools immediately especially with the growing DDoS threats. This is critical for active defence against proliferation of attack tools. The project aims to provide network managers and cybersecurity professionals with the tools required to ensure the security of online services. Finally, it offers the users and organisations resilient protection against the new generations of DDoS attacks. We also used ensemble models (VC (RF, DT) and Stacking Classifier (RF, DT, LGBM)) to improve the performance. These enhancements aim to enhance the ability to identify incidents of cyberbullying.