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An adaptive system for detecting malicious queries in web attacks

  • Ying Dong
  • Yuqing Zhang
  • Hua Ma
  • Qianru Wu
  • Qixu Liu
  • Kai Wang
  • Wenjie Wang
Research Paper

Abstract

Web request query strings (queries), which pass parameters to a referenced resource, are always manipulated by attackers to retrieve sensitive data and even take full control of victim web servers and web applications. However, existing malicious query detection approaches in the literature cannot cope with changing web attacks. In this paper, we introduce a novel adaptive system (AMOD) that can adaptively detect web-based code injection attacks, which are the majority of web attacks, by analyzing queries. We also present a new adaptive learning strategy, called SVM HYBRID, leveraged by our system to minimize manual work. In the evaluation, an up-to-date detection model is trained on a ten-day query dataset collected from an academic institute’s web server logs. The evaluation shows our approach overwhelms existing approaches in two respects. Firstly, AMOD outperforms existing web attack detection methods with an F-value of 99.50% and FP rate of 0.001%. Secondly, the total number of malicious queries obtained by SVM HYBRID is 3.07 times that by the popular support vector machine adaptive learning (SVM AL) method. The malicious queries obtained can be used to update the web application firewall (WAF) signature library.

Keywords

web attacks adaptive learning intrusion detection anomaly detection SVM 

Notes

Acknowledgements

This work was supported in part by National Key Reasearch and Development Program of China (Grant No. 2016YFB0800703), in part by National Natural Science Foundation of China (Grant Nos. 61272481, 61572460), in part by Open Project Program of the State Key Laboratory of Information Security (Grant Nos. 2017-ZD-01, 2016-MS-02), and in part by National Information Security Special Project of the National Development and Reform Commission of China (Grant No. (2012)1424). We would also like to thank Dr. Xinyu Xing in Pennsylvania State University for his help with this work.

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Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Ying Dong
    • 1
  • Yuqing Zhang
    • 1
    • 2
  • Hua Ma
    • 2
    • 3
  • Qianru Wu
    • 4
  • Qixu Liu
    • 1
    • 2
  • Kai Wang
    • 5
  • Wenjie Wang
    • 1
  1. 1.National Computer Network Intrusion Protection CenterUniversity of Chinese Academy of SciencesBeijingChina
  2. 2.State Key Laboratory of Information Security, Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  3. 3.School of Mathematics and StatisticsXidian UniversityXi’anChina
  4. 4.Security DepartmentAlibaba GroupBeijingChina
  5. 5.Zhanlu LaboratoryTencent IncorporationBeijingChina

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