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

Webshell作为众多网络攻击的“罪魁祸首”，是网络安全领域的研究热点之一。然而，Webshell的复杂性、隐蔽性和混淆性给相应的检测方案带来了巨大挑战。随着人工智能技术的兴起，研究人员开始将不同的智能算法和神经网络架构应用于Webshell检测任务。然而，相关研究仍缺乏系统化和标准化的方法论过程，显得混乱而冗余。因此，我们按照发展时间线，仔细总结了该领域相关研究的进展，将其分为三个阶段：起始阶段、初步发展阶段和深入发展阶段。我们进一步阐述了每个阶段的主要特点和核心算法。此外，我们分析了该领域仍存在的痛点和挑战，并从我们的角度预测了该领域未来的发展趋势。据我们所知，这是第一篇详细介绍基于人工智能的Webshell检测相关研究的综述。我们也希望本文能为更多对基于人工智能的Webshell检测任务感兴趣的研究人员提供详细的技术信息。

AI-based webshell detection

The paper summarizes the progress of AI-based webshell detection research and analyzes the pain points and challenges that still exist in this field. It also predicts the future development trend of this field.

The paper divides the development of AI-based webshell detection research into three stages: Start Stage, Initial Development Stage, and In-depth Development Stage. It elaborates on the main characteristics and core algorithms of each stage. The paper provides detailed technical information for researchers interested in AI-based webshell detection tasks.

Recent related research includes 'Webshell Detection Using Machine Learning Techniques: A Systematic Literature Review' and 'Webshell Detection Using Machine Learning: A Survey'.