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PhishReplicant: A Language Model-based Approach to Detect Generated Squatting Domain Names.

, , , and . ACSAC, page 1-13. ACM, (2023)

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Canary in Twitter Mine: Collecting Phishing Reports from Experts and Non-experts., , , , , , , and . ARES, page 6:1-6:12. ACM, (2023)A Large-scale Analysis of Cloud Service Abuse., , , and . CNS, page 1-9. IEEE, (2020)Noisy Label Detection for Multi-labeled Malware., , , , and . CCNC, page 165-171. IEEE, (2024)A Comprehensive Measurement of Cloud Service Abuse., , , and . J. Inf. Process., (2021)PhishReplicant: A Language Model-based Approach to Detect Generated Squatting Domain Names., , , and . ACSAC, page 1-13. ACM, (2023)Analyzing Security Risks of Ad-Based URL Shortening Services Caused by Users' Behaviors., , , , and . SecureComm (2), volume 399 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, page 3-22. Springer, (2021)Exploration into Gray Area: Efficient Labeling for Malicious Domain Name Detection., , , and . COMPSAC (1), page 770-775. IEEE, (2019)Exploration into Gray Area: Toward Efficient Labeling for Detecting Malicious Domain Names., , , and . IEICE Trans. Commun., 103-B (4): 375-388 (2020)Understanding Characteristics of Phishing Reports from Experts and Non-Experts on Twitter., , , , , , , and . IEICE Trans. Inf. Syst., 107 (7): 807-824 (2024)