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Xiao, X., Zhang, D., Hu, G., Jiang, Y. and Xia, S. (2020) CNN–MHSA: A Convolutional Neural Network and Multi-Head Self-Attention Combined Approach for Detecting Phishing Websites. Neural Networks, 125, 303-312.
https://doi.org/10.1016/j.neunet.2020.02.013

has been cited by the following article:

• TITLE: PhishGuard: Integrating Fine-Tuned Large Language Models (LLMs) into Password Management

  AUTHORS: Smith Patel, Vijay K. Madisetti

  KEYWORDS: Phishing Attacks, Password Management, Phishing Detection, AI, BERT Algorithm, Real-Time Protection, Cybersecurity, URL Authentication

  JOURNAL NAME: Journal of Information Security, Vol.15 No.4, October 10, 2024

  ABSTRACT: In the digital age, phishing attacks have been a persistent security threat leveraged by traditional password management systems that are not able to verify the authenticity of websites. This paper presents an approach to embedding sophisticated phishing detection within a password manager’s framework, called PhishGuard. PhishGuard uses a Large Language Model (LLM), specifically a fine-tuned BERT algorithm that works in real time, where URLs fed by the user in the credentials are analyzed and authenticated. This approach enhances user security with its provision of real-time protection from phishing attempts. Through rigorous testing, this paper illustrates how PhishGuard has scored well in tests that measure accuracy, precision, recall, and false positive rates.