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Detection of SQL Injection Attack Using Machine Learning Techniques: A Review

  • Unique Paper ID: 176945
  • Volume: 11
  • Issue: 11
  • PageNo: 6546-6552
  • Abstract:
  • SQL Injection (SQLi) attacks pose a significant threat to database security, enabling attackers to manipulate SQL queries and gain unauthorized access to sensitive data. Traditional security measures, such as signature-based detection and rule-based approaches, often fail to detect evolving SQLi attack patterns. To address these challenges, machine learning (ML) techniques have emerged as powerful tools for detecting and mitigating SQLi attacks. This review paper explores various ML-based approaches, including supervised, unsupervised, and deep learning models, for identifying SQLi attempts. It examines feature extraction methods, dataset challenges, model performance metrics, and comparative analyses of existing ML techniques. Additionally, the paper highlights the advantages and limitations of different ML models in real-world scenarios, emphasizing their effectiveness in improving detection accuracy and reducing false positives.
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  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 11
  • PageNo: 6546-6552

Detection of SQL Injection Attack Using Machine Learning Techniques: A Review

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