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Mitigating Webshell Attacks through Machine Learning Techniques

1
School of Computing Science and Engineering, Xi’an Technological University, Xi’an 710021, China
2
School of Computing, Engineering and Physical Sciences, University of the West of Scotland, High Street, Paisley PA1 2BE, UK
*
Author to whom correspondence should be addressed.
Future Internet 2020, 12(1), 12; https://doi.org/10.3390/fi12010012
Received: 10 December 2019 / Revised: 24 December 2019 / Accepted: 2 January 2020 / Published: 14 January 2020
(This article belongs to the Special Issue Security and Privacy in Social Networks and Solutions)
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. View Full-Text
Keywords: webshell attacks; machine learning; naïve Bayes; opcode sequence webshell attacks; machine learning; naïve Bayes; opcode sequence
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Guo, Y.; Marco-Gisbert, H.; Keir, P. Mitigating Webshell Attacks through Machine Learning Techniques. Future Internet 2020, 12, 12.

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