Earticle

초록

영어

Modern web application systems are generally consisted of database systems in order to process and store business information. These systems are highly interesting to hackers as they contain sensitive information and the diversity and amount of attacks severely undermine the effectiveness of classical signature-based detection. In this work we propose a novel approach for learning SQL statements and apply machine learning techniques, such as one class classification, in order to detect malicious behavior between the database and application. The approach incorporates the tree structure of SQL queries as well as input parameter and query value similarity as characteristic to distinguish malicious from benign queries. We develop the learning system integrated in PHP and demonstrate the usefulness of our approach on real-world application.

목차

Abstract
 1. Introduction
 2. SVM Kernel Tricks and Tree-vector Kernel
  2.1. SVM kernel Tricks
  2.2. Tree-vector Kernel for Structured Data
 3. Kernel Function for SQL Query
  3.1. Tree Kernel Function
  3.2. Vector Kernel Function
 4. System Design and Evaluation
  4.1. System Design
  4.2. Evaluation
 5. Conclusion
 References

키워드

저자

• Yi Wang [ State Key Laboratory of Software Development Environment, Beihang University ]
• Zhoujun Li [ State Key Laboratory of Software Development Environment, Beihang University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605