MWAD: A Multimodal Web Attack Dataset for AI-Driven SQL Injection Detection

Overview

The Multimodal Web Attack Dataset (MWAD) is a novel labelled cybersecurity dataset developed as part of the author's research in cybersecurity at La Trobe University, Australia.

The dataset generation methodology, design, and description are presented in the peer-reviewed journal article:

Yeboah, P. N., et al. (2026). SQL injection detection using self-supervised pre-training and multimodal techniques. Intelligent Systems with Applications, 31, 200702.
https://doi.org/10.1016/j.iswa.2026.200702

Unlike traditional web attack datasets that typically contain only HTTP requests or network traffic, MWAD integrates application-layer HTTP request data with network flow features, providing a multimodal representation of web attacks.

MWAD is designed to support the development and evaluation of artificial intelligence (AI) and machine learning (ML) models for web attack detection, with a particular focus on SQL injection (SQLi). By combining complementary data modalities, the dataset enables researchers to investigate multimodal learning approaches that improve the accuracy and robustness of AI-driven web attack detection.

Each MWAD sample combines two complementary data modalities:

1. HTTP request data, representing the content and structure of web requests.
2. Network-flow features, representing the behavioural characteristics of the corresponding network traffic.

Dataset Generation Pipeline

The dataset was generated in a controlled private-network environment using a vulnerable web server hosted on a Metasploitable virtual machine.

SQL injection attacks were executed using SQLMap and SQL injection payloads. The resulting traffic was collected and processed using the following tools:

• Wireshark for HTTP request logging
• Tcpdump for packet capture
• NFStream for network-flow feature extraction

The HTTP request data and corresponding flow-level features were subsequently labelled and combined to produce the final multimodal dataset.

Figure 1. MWAD generation pipeline showing SQL injection execution, HTTP request collection, packet capture, flow-feature extraction and multimodal data integration.

Dataset Composition

Data Modalities

HTTP Request Modality

The HTTP modality contains request-level information generated during interactions with the vulnerable web application.

This modality can support:

• textual analysis of HTTP requests
• malicious payload detection
• token-based machine learning
• deep learning and transformer-based modelling
• self-supervised representation learning

Network-Flow Modality

The network-flow modality contains statistical and behavioural features extracted from captured traffic using NFStream.

This modality can support:

• traffic-flow classification
• anomaly detection
• behavioural attack analysis
• conventional machine learning
• multimodal fusion with HTTP request representations

Key Features

• Publicly available multimodal cybersecurity dataset
• Contains paired HTTP-request and network-flow information
• Includes clearly labelled benign and SQL injection traffic
• Balanced dataset containing 500 samples in each class
• Generated through controlled SQL injection experiments
• Supports reproducible cybersecurity research
• Suitable for unimodal and multimodal model evaluation
• Applicable to machine learning, deep learning and self-supervised learning

Dataset Novelty

Many web-attack datasets provide either application-layer request information or network-level traffic features separately.

MWAD was developed to provide paired application-layer and network-flow representations of the same web interactions. This enables researchers to examine whether combining the two modalities improves SQL injection detection compared with relying on either modality independently.

The dataset therefore provides a reusable research resource for investigating multimodal learning and data-fusion strategies in web-attack detection.

Associated Publication

The MWAD dataset generation methodology, design, and evaluation are presented in the following peer-reviewed publication:

Intelligent Systems with Applications, Elsevier.

Citation

If you use MWAD in your research, please cite:

@article{yeboah2026sql,
  title={SQL injection detection using self-supervised pre-training and multimodal techniques},
  author={Yeboah, Paul Ntim and Kayes, A. S. M. and Rahayu, Wenny and Pardede, Eric and Mahbub, Syed},
  journal={Intelligent Systems with Applications},
  volume={31},
  pages={200702},
  year={2026},
  publisher={Elsevier},
  doi={10.1016/j.iswa.2026.200702}
}