LMs go Phishing: Adapting Pre-trained Language Models to Detect Phishing Emails
Author:
Affiliation:
1. Purdue University,Department of CIT,West Lafayette,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10101871/10101874/10101955.pdf?arnumber=10101955
Reference37 articles.
1. Catbert: Context-aware tiny bert for detecting social engineering emails;lee,2020
2. Comparison of the predicted and observed secondary structure of T4 phage lysozyme
3. Phishing Email Detection Using Improved RCNN Model With Multilevel Vectors and Attention Mechanism
4. One billion word benchmark for measuring progress in statistical language modeling;chelba,2013
5. Learning from the Ones that Got Away: Detecting New Forms of Phishing Attacks
Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. A Review of Advancements and Applications of Pre-Trained Language Models in Cybersecurity;2024 12th International Symposium on Digital Forensics and Security (ISDFS);2024-04-29
2. Devising and Detecting Phishing Emails Using Large Language Models;IEEE Access;2024
3. Detecting Phishing URLs With Word Embedding and Deep Learning;Advances in Systems Analysis, Software Engineering, and High Performance Computing;2023-06-30
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