6 papers
PhishParrot: LLM-Driven Adaptive Crawling to Unveil Cloaked Phishing Sites
Hiroki Nakano, Takashi Koide, Daiki Chiba
Phishing attacks continue to evolve, with cloaking techniques posing a significant challenge to detection efforts. Cloaking allows attackers to display phishing sites only to speci…
DomainDynamics: Lifecycle-Aware Risk Timeline Construction for Domain Names
Daiki Chiba, Hiroki Nakano, Takashi Koide
The persistent threat posed by malicious domain names in cyber-attacks underscores the urgent need for effective detection mechanisms. Traditional machine learning methods, while c…
ScamFerret: Detecting Scam Websites Autonomously with Large Language Models
Hiroki Nakano, Takashi Koide, Daiki Chiba
With the rise of sophisticated scam websites that exploit human psychological vulnerabilities, distinguishing between legitimate and scam websites has become increasingly challengi…
DomainLynx: Leveraging Large Language Models for Enhanced Domain Squatting Detection
Daiki Chiba, Hiroki Nakano, Takashi Koide
Domain squatting poses a significant threat to Internet security, with attackers employing increasingly sophisticated techniques. This study introduces DomainLynx, an innovative co…
Detecting Phishing Sites Using ChatGPT
Takashi Koide, Naoki Fukushi, Hiroki Nakano +1
The emergence of Large Language Models (LLMs), including ChatGPT, is having a significant impact on a wide range of fields. While LLMs have been extensively researched for tasks su…
DomainHarvester: Harvesting Infrequently Visited Yet Trustworthy Domain Names
Daiki Chiba, Hiroki Nakano, Takashi Koide
In cybersecurity, allow lists play a crucial role in distinguishing safe websites from potential threats. Conventional methods for compiling allow lists, focusing heavily on websit…