WebShell Detection Based on CodeBERT and Deep Learning Model-Reference-Cited by-同舟云学术

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WebShell Detection Based on CodeBERT and Deep Learning Model

Published:2024-05-24 Issue: Volume:11344 Page:484-489
ISSN:
Container-title:Proceedings of the 2024 5th International Conference on Computing, Networks and Internet of Things
language:
Short-container-title:

Author:

Wang Guan-Yu¹^ORCID,Ko Hung-Jui²^ORCID,Chiang Chang-Po³^ORCID,Wang Wei-Jen¹^ORCID

Affiliation:

1. Department of Computer Science and Information Engineering, National Central University, Taiwan

2. Changhua County Police Department, Taiwan

3. Department of Crime Prevention and Corrections, Central Police University, Taiwan

Funder

NSTC

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3670105.3670190

Reference19 articles.

1. Acunetix. 2021. Spring 2021 Edition: Acunetix Web Vulnerability Report. Retrieved January 15, 2024, from https://www.acunetix.com/white-papers/acunetix-web-application-vulnerability-report-2021/.

2. Kaspersky. 2021. PHP language source code compromise attempt. Retrieved January 15, 2024, from https://www.kaspersky.com/blog/php-git-backdor/39191/.

3. Feng Z. Guo D. Tang D. Duan N. Feng X. Gong M. Shou L. Qin B. Liu T. and Jiang D. 2020. CodeBERT: A Pre-Trained Model for Programming and Natural Languages. arXiv preprint arXiv:2002.08155.

4. Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. 2018. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.

5. Liu Y. Ott M. Goyal N. Du J. Joshi M. Chen D. Levy O. Lewis M. Zettlemoyer L. and Stoyanov V. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.

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