Webshell detection method based on forest optimization feature selection algorithm

To address the increasing diversity in the forms and complexity of Webshells in recent years, as well as the challenge of high-dimensional feature sets, this paper proposes a Webshell detection algorithm based on the Improved Forest Optimization Algorithm (IFSFOA). This algorithm employs a process of random tree reduction and utilizes an adaptive parameter selection strategy to perform feature selection on the combined features of text characteristics and opcode at the compilation layer. It effectively filters out low-dimensional Webshell features, thereby enhancing the representational capability in feature dimensions. Experimental results demonstrate that the Webshell detection method based on the Improved Forest Optimization feature selection algorithm outperforms commonly used Webshell feature handling methods in terms of both detection rate and recall rate in recent years.