Call For Paper Volume: V, Issue: 08 | AUGUST 2026 | International Journal of Advanced Trends in Engineering and Management (IJATEM)
Volume V | Issue 5 | 2026 | Paper ID: IJATEM-V05I05P3

Intelligent Web Attack Classification Using a Hybrid Triple Attention–Dual Mamba Attention Network with Dandelion Optimization

, Ramya R R

Web attack Classification is critical for stopping attacks early, which helps prevent data loss, minimize operational downtime, and avoid paying expensive ransoms. In this paper, Hybrid Triple Attention-Dual Mamba Attention Network (HTA-DMAN) is proposed with Dandelion Optimization (DLO) for web attack classification. Data preprocessing turns messy raw data into a clean, structured format ready for analysis. Feature engineering focuses on extracting relevant features and scaling data, leading to the application of a classification model, specifically an HTA-DMAN-DLO approach. HTA-DMAN operates through coordinated functional stages to classify complex inputs like web attack and DLO optimization is used to tune hyperparameters of classifier. The performance analysis is carried out using python software and assesses the model's performance using key metrics such as precision, recall, F1-score, confusion matrix, and ROC-AUC. The proposed HTA-DMAN-DLO uses two classes, they are normal and attack. The proposed HTA-DMAN-DLO achieves high accuracy of 96% and better convergence speed. The proposed method outperforms conventional techniques, contributing a robust and reliable tool for web attack classification.

Hybrid Triple Attention-Dual Mamba Attention Network (HTA-DMAN), Dandelion Optimization (DLO), Web attack Classification, convergence speed.

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