mlsanityData to 14 Oct 2026

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

arXiv 2405.00066 ↗First posted 28 Apr 2024cs.CR cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 33 of its 93 references that are in the map. First posted on arXiv 28 Apr 2024.

Builds on

33 of 93 references.

References · 33

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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Poster: Long PHP webshell files detection based on sliding window attentioncited by 2
  2. 2025A Reward-driven Automated Webshell Malicious-code Generator for Red-teamingcited by 1
  3. 2025Beyond Detection: A Comprehensive Benchmark and Study on Representation Learning for Fine-Grained Webshell Family Classificationcited by 1
  4. 2026ShellForge: Adversarial Co-Evolution of Webshell Generation and Multi-View Detection for Robust Webshell Defensecited by 0