A detection methodology for SQL injection attacks based on the TF-IDF-CHI algorithm

Liu, Runjie; Zhang, Wenbo

doi:10.1117/12.3011777

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2024

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“…In [21], the authors studied how data preparation and feature extraction techniques based on TF-IDF influenced detection accuracy of malicious SQL queries. [16] described the use of TF-IDF-CHI algorithm in SQL Injection Attack detection.…”

Section: Related Workmentioning

confidence: 99%

Learning From User-Specified Optimizer Hints in Database Systems

Zakrzewicz

2024

Foundations of Computing and Decision Sciences

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Recently, numerous machine learning (ML) techniques have been applied to address database performance management problems, including cardinality estimation, cost modeling, optimal join order prediction, hint generation, etc. In this paper, we focus on query optimizer hints employed by users in their queries in order to mask some Query Optimizer deficiencies. We treat the query optimizer hints, bound to previous queries, as significant additional query metadata and learn to automatically predict which new queries will pose similar performance challenges and should therefore also be supported by query optimizer hints. To validate our approach, we have performed a number of experiments using real-life SQL workloads and we achieved promising results.

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Section: Related Workmentioning

confidence: 99%

Learning From User-Specified Optimizer Hints in Database Systems

Zakrzewicz

2024

Foundations of Computing and Decision Sciences

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A detection methodology for SQL injection attacks based on the TF-IDF-CHI algorithm

Cited by 1 publication

References 7 publications

Learning From User-Specified Optimizer Hints in Database Systems

Learning From User-Specified Optimizer Hints in Database Systems

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