Superviz26-SQL: A Multi-Domain Benchmark for SQL Attack Detection
Year: 2026
Type: conference-paper
Abstract: Machine-learning intrusion detectors that excel on a testbed often fail once the deployment environment changes. Yet comparable multi-domain datasets with controlled distribution shifts remain scarce. This holds for SQL attack detection (SQLAD), whose public clear-text data covers only single applications, leaving the cross-domain behaviour of detection methods unmeasured. We introduce Superviz26-SQL, the first multi-domain SQLAD benchmark, extending the synthetic generation methodology of Superviz25-SQL to four heterogeneous domains derived from real-world database projects. From these domains we derive ready-to-use datasets for three evaluation protocols: Superviz26-SQL-LODO for cross-domain generalisation, Superviz26-SQL-CD for samedomain concept drift, and Superviz26-SQL-FSL for few-shot adaptation. Lexical-field analysis confirms genuine inter-domain diversity, and each training set is large enough to train deep detectors. We illustrate each protocol using reference baselines spanning five feature extractors and three decision engines. Superviz26-SQL, its three derivatives, and all generation and experiment code are publicly released to support reproducible cross-domain SQLAD research.
Source: HAL
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204608324