Multi-Class Intrusion Detection System using Deep Learning

Abstract

Web applications are a critical means of accessing information in today's world.However, as the number of internet users continues to grow rapidly, cybersecurity has become a major concern.In this study, a deep learning-based approach to detect web attacks is proposed.Our system explores incoming requests, categorizing them as either normal or attacks, and further identifies the type of attack.The approach is evaluated on three different datasets (ECML-PKDD, HTTPPARAM, and CSIC-2012) and used four classification algorithms (Bi-LSTM, LSTM, RNN, and CNN).The Bi-LSTM algorithm achieves high accuracy with the ECML-PKDD and HTTPPARAM datasets (90.6% and 99.66%, respectively), while the CNN algorithm performs best with the CSIC-2012 dataset, achieving an accuracy of 99.28%.This research provides a valuable contribution to the field of web security and has practical applications for companies and website owners who need to protect their data from potential attacks, making it a powerful tool in the fight against cybercrime.