5 papers
PhishGen: Understanding LLM Capabilities in Automated Phishing Website Generation
Fujiao Ji, Yerim Kim, Yena Cho +3
Phishing relies on visual deception, traditionally requiring web-development skills or pre-built kits. LLMs erode this barrier by generating functional websites from natural-langua…
A Large-Scale Empirical Study of Modern Phishing Email Content
Jaehwan Park, Woonghee Lee, Fujiao Ji +1
Phishing remains one of the most pervasive threats to Internet users, and email remains its predominant delivery channel. Email content is the attack surface of phishing: it is wha…
Poster: Mind the Gap -- Characterizing the Temporal Blind Spot Between GSB and DNS Resolution
Tomer Gal, Fujiao Ji, Doowon Kim +1
Google Safe Browsing (GSB) and DNS resolution operate concurrently during browser navigation, yet their packet-level synchronization remains understudied. This work characterizes t…
How Can We Effectively Use LLMs for Phishing Detection?: Evaluating the Effectiveness of Large Language Model-based Phishing Detection Models
Fujiao Ji, Doowon Kim
Large language models (LLMs) have emerged as a promising phishing detection mechanism, addressing the limitations of traditional deep learning-based detectors, including poor gener…
Evaluating the Effectiveness and Robustness of Visual Similarity-based Phishing Detection Models
Fujiao Ji, Kiho Lee, Hyungjoon Koo +4
Phishing attacks pose a significant threat to Internet users, with cybercriminals elaborately replicating the visual appearance of legitimate websites to deceive victims. Visual si…