Poster: Long PHP webshell files detection based on sliding window attention
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929733746294784 |
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| author | Wang, Zhiqiang Wang, Haoyu Hao, Lu |
| author_facet | Wang, Zhiqiang Wang, Haoyu Hao, Lu |
| contents | Webshell is a type of backdoor, and web applications are widely exposed to webshell injection attacks. Therefore, it is important to study webshell detection techniques. In this study, we propose a webshell detection method. We first convert PHP source code to opcodes and then extract Opcode Double-Tuples (ODTs). Next, we combine CodeBert and FastText models for feature representation and classification. To address the challenge that deep learning methods have difficulty detecting long webshell files, we introduce a sliding window attention mechanism. This approach effectively captures malicious behavior within long files. Experimental results show that our method reaches high accuracy in webshell detection, solving the problem of traditional methods that struggle to address new webshell variants and anti-detection techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19257 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Poster: Long PHP webshell files detection based on sliding window attention Wang, Zhiqiang Wang, Haoyu Hao, Lu Cryptography and Security Artificial Intelligence Webshell is a type of backdoor, and web applications are widely exposed to webshell injection attacks. Therefore, it is important to study webshell detection techniques. In this study, we propose a webshell detection method. We first convert PHP source code to opcodes and then extract Opcode Double-Tuples (ODTs). Next, we combine CodeBert and FastText models for feature representation and classification. To address the challenge that deep learning methods have difficulty detecting long webshell files, we introduce a sliding window attention mechanism. This approach effectively captures malicious behavior within long files. Experimental results show that our method reaches high accuracy in webshell detection, solving the problem of traditional methods that struggle to address new webshell variants and anti-detection techniques. |
| title | Poster: Long PHP webshell files detection based on sliding window attention |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2502.19257 |