• DocumentCode
    3580259
  • Title

    Improving Web Application Firewalls to detect advanced SQL injection attacks

  • Author

    Makiou, Abdelhamid ; Begriche, Youcef ; Serhrouchni, Ahmed

  • Author_Institution
    Telecom ParisTech, Paris, France
  • fYear
    2014
  • Firstpage
    35
  • Lastpage
    40
  • Abstract
    Injections flaws which include SQL injection are the most prevalent security threats affecting Web applications[1]. To mitigate these attacks, Web Application Firewalls (WAFs) apply security rules in order to both inspect HTTP data streams and detect malicious HTTP transactions. Nevertheless, attackers can bypass WAF´s rules by using sophisticated SQL injection techniques. In this paper, we introduce a novel approach to dissect the HTTP traffic and inspect complex SQL injection attacks. Our model is a hybrid Injection Prevention System (HIPS) which uses both a machine learning classifier and a pattern matching inspection engine based on reduced sets of security rules. Our Web Application Firewall architecture aims to optimize detection performances by using a prediction module that excludes legitimate requests from the inspection process.
  • Keywords
    Internet; SQL; firewalls; learning (artificial intelligence); pattern classification; pattern matching; telecommunication traffic; transport protocols; HIPS; HTTP data streams; WAF rules; Web application firewall architecture; complex SQL injection attack detection; hybrid injection prevention system; legitimate requests; machine learning classifier; malicious HTTP transaction detection; pattern matching inspection engine; prediction module; reduced sets; security rules; security threats; Servers; HTTP dissection; SQL injection; Security rules; Web Application Firewall; machine learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Assurance and Security (IAS), 2014 10th International Conference on
  • Print_ISBN
    978-1-4799-8098-7
  • Type

    conf

  • DOI
    10.1109/ISIAS.2014.7064617
  • Filename
    7064617