Advancing Web Security: Machine Learning-Based Attack Detection with Optimized Features

Abstract

Web applications remain highly susceptible to cyberattacks despite efforts to mitigate these threats through initiatives like the OWASP Top 10. This research addresses the critical challenge of improving web attack detection by integrating advanced feature extraction techniques and machine learning (ML) models. A novel approach was developed, consisting of three main contributions: the design of a testbed attack network to simulate real-world scenarios, the application of a wrapper-based feature extraction method combining mutual information (MI) and genetic algorithms (GA) to select pertinent traffic features, and the implementation of a super learning ensemble model for robust attack detection. The feature selection method extracted features included critical traffic attributes, which significantly enhanced the model's detection capabilities. The proposed ensemble-based super learner model achieved a remarkable accuracy of 99.12% with a reduced prediction time of 125 milliseconds. Compared to conventional ML models, the proposed model demonstrated a 26% improvement in detection accuracy and a 99% reduction in prediction time, making it highly efficient for real-time web attack detection.