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Mitigating Webshell Attacks through Machine Learning Techniques

Author

Listed:
  • You Guo

    (School of Computing Science and Engineering, Xi’an Technological University, Xi’an 710021, China)

  • Hector Marco-Gisbert

    (School of Computing, Engineering and Physical Sciences, University of the West of Scotland, High Street, Paisley PA1 2BE, UK)

  • Paul Keir

    (School of Computing, Engineering and Physical Sciences, University of the West of Scotland, High Street, Paisley PA1 2BE, UK)

Abstract

A webshell is a command execution environment in the form of web pages. It is often used by attackers as a backdoor tool for web server operations. Accurately detecting webshells is of great significance to web server protection. Most security products detect webshells based on feature-matching methods—matching input scripts against pre-built malicious code collections. The feature-matching method has a low detection rate for obfuscated webshells. However, with the help of machine learning algorithms, webshells can be detected more efficiently and accurately. In this paper, we propose a new PHP webshell detection model, the NB-Opcode (naïve Bayes and opcode sequence) model, which is a combination of naïve Bayes classifiers and opcode sequences. Through experiments and analysis on a large number of samples, the experimental results show that the proposed method could effectively detect a range of webshells. Compared with the traditional webshell detection methods, this method improves the efficiency and accuracy of webshell detection.

Suggested Citation

  • You Guo & Hector Marco-Gisbert & Paul Keir, 2020. "Mitigating Webshell Attacks through Machine Learning Techniques," Future Internet, MDPI, vol. 12(1), pages 1-16, January.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:1:p:12-:d:308719
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    Citations

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    Cited by:

    1. Yilin Wang & Siqing Xue & Jun Song, 2022. "A Malicious Webpage Detection Method Based on Graph Convolutional Network," Mathematics, MDPI, vol. 10(19), pages 1-15, September.

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