Webshell combination detection method based on Naïve Bayes and decision tree

Fei Ren

doi:10.1117/12.3025714

28 February 2024 Webshell combination detection method based on Naïve Bayes and decision tree

Fei Ren

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 130712Y (2024) https://doi.org/10.1117/12.3025714
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

The paper aims to enhance the recall rate of Webshell detection methods based on machine learning algorithms. It covers a broad research scope on Webshells, with a focus on Webshells as the research object. The research employs a combination of the Naive Bayes and Decision Tree methods, extracting and analyzing static features of Webshells. Significant conclusions regarding the improvement of recall rates are drawn from this analysis. Experimental validation conducted on publicly available datasets demonstrates that the proposed method significantly increases the recall rate of the Webshell detection model while maintaining a low false-positive rate. This improvement holds crucial significance for enhancing the security of web applications and servers, offering a feasible research approach in the field of Webshell detection.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fei Ren "Webshell combination detection method based on Naïve Bayes and decision tree", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 130712Y (28 February 2024); https://doi.org/10.1117/12.3025714

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KEYWORDS

Decision trees

Detection and tracking algorithms

Data modeling

Education and training

Feature extraction

Machine learning

Network security

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