WebShell Detection Based on CodeBERT and Deep Learning Model

Guan Yu Wang, Hung Jui Ko, Chang Po Chiang, Wei Jen Wang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

1 引文 斯高帕斯(Scopus)

摘要

The web shell attacks (WebShells) have long been a source of persistent annoyance for administrators. They have become a major security concern in cloud computing environments since the scalability and distributed nature of the cloud services could intensify the potential risks and impacts of such attacks. In response, researchers have proposed numerous strategies to shield assets from WebShell intrusions. Consequently, this study proposes a method that utilizes the BPE (Byte Pair Encoding) for tokenization, the CodeBERT for extracting the word embedding vector of a given source code piece, and a deep model (GRU or Bidirectional GRU) for determining whether the code contains a WebShell. This architecture is designed to detect the presence of WebShells in PHP code through analysis of the source code. Our experimental results indicate that, the proposed method with GRU achieves the best performance, with an accuracy of 99.72%, a precision of 99.36%, and an F1-score of 99.36%. Furthermore, it outperforms the methods proposed in the prior related studies as presented in the paper.

原文???core.languages.en_GB???
主出版物標題CNIOT 2024 - Conference Proceeding, 2024 5th International Conference on Computing, Networks and Internet of Things
發行者Association for Computing Machinery
頁面484-489
頁數6
ISBN(電子)9798400716751
DOIs
出版狀態已出版 - 24 5月 2024
事件5th International Conference on Computing, Networks and Internet of Things, CNIOT 2024 - Tokyo, Japan
持續時間: 24 5月 202426 5月 2024

出版系列

名字ACM International Conference Proceeding Series

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???event.eventtypes.event.conference???5th International Conference on Computing, Networks and Internet of Things, CNIOT 2024
國家/地區Japan
城市Tokyo
期間24/05/2426/05/24

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