Application of Robust Software Modelling Tool for Web Attacks Detection

Abstract

Abstract: The current inquiry as an examination of web attacks where proliferation of web-based applications has brought about a concurrent rise in cyber threats, particularly the form of web attacks targeting vulnerable systems. Approaches to web attack detection often rely on rule-based or signature-based methods, which struggle to change with the increasing landscape of attacks. In response, this study proposes an innovative approach leveraging DL techniques for web attacks. By harnessing the capability of DL, especially CNN and recurrent neural networks (RNNs), our proposed system learns directly from raw web traffic data, eliminating the need for manual feature engineering. This end-to-end approach not only streamlines the detection process but also enhances the system's ability to generalize across different types of attacks and adapt to new threats. To evaluate the effectiveness of our approach, we conducted extensive experiments on diverse datasets containing both benign and malicious web traffic. Our results demonstrate the superiority of end-to-end deep learning over traditional methods, achieving higher detection accuracy and robustness against adversarial attacks. In conclusion, our study highlights the promise of end-to-end deep learning as a viable approach related to web attacks, offering enhanced detection capabilities in the phase of evolving cyber threats.