A Novel Deep Learning Approach with Magnet Loss Optimization for Website Attack Detection

Detecting attacks on websites is crucial for maintaining their security and availability, particularly against common OWASP threats. Conventional detection systems typically depend on rule-based approaches and signature matching, restricting their ability to detect new or emerging threats. Recently, various models have been developed using machine learning (ML) models to detect these attacks. However, these models still face significant challenges, including low accuracy, high false positive rates, and an inability to generalize to new attack types. In this paper, we propose a novel model that combines the Deep Auto-Encoder (DAE) architecture with the concept of Magnet Loss in the latent space. The DAE transforms input data into a lower-dimensional representation, capturing complex and nonlinear relationships between features. By incorporating Magnet Loss, the model maximizes the margin between normal and attack regions in latent space. This separation allows for more precise identification of attacks, even previously unseen or involving subtle variations of known techniques. Experiments conducted on the datasets, such as CIC-IDS-2017 and CSE-CIC-IDS-2018, show that the proposed model achieves higher Accuracy, Precision, Recall, and F1-Score than previous approaches while notably decreasing computation time during the testing phase.