Research and application of artificial intelligence based webshell detection model: A literature review

TL;DR

This literature review summarizes the development, challenges, and future trends of AI-based webshell detection methods, highlighting the evolution of algorithms and the need for standardized research processes in cybersecurity.

Contribution

First comprehensive review of AI-based webshell detection research, categorizing progress into development stages and analyzing core algorithms and challenges.

Findings

Webshell detection is crucial for cybersecurity.

AI algorithms are increasingly applied to detect webshells.

Future research should focus on standardization and overcoming existing challenges.

Abstract

Webshell, as the "culprit" behind numerous network attacks, is one of the research hotspots in the field of cybersecurity. However, the complexity, stealthiness, and confusing nature of webshells pose significant challenges to the corresponding detection schemes. With the rise of Artificial Intelligence (AI) technology, researchers have started to apply different intelligent algorithms and neural network architectures to the task of webshell detection. However, the related research still lacks a systematic and standardized methodological process, which is confusing and redundant. Therefore, following the development timeline, we carefully summarize the progress of relevant research in this field, dividing it into three stages: Start Stage, Initial Development Stage, and In-depth Development Stage. We further elaborate on the main characteristics and core algorithms of each stage. In addition, we analyze the pain points and challenges that still exist in this field and predict the future development trend of this field from our point of view. To the best of our knowledge, this is the first review that details the research related to AI-based webshell detection. It is also hoped that this paper can provide detailed technical information for more researchers interested in AI-based webshell detection tasks.