PhishBench 2.0: A Versatile and Extendable Benchmarking Framework for Phishing
| Autor: | Victor Zeng, Rakesh M. Verma, Shahryar Baki, Xin Zhou |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry 02 engineering and technology Phishing detection Benchmarking Machine learning computer.software_genre Phishing Ranking (information retrieval) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | CCS |
| DOI: | 10.1145/3372297.3420017 |
| Popis: | We describe version 2.0 of our benchmarking framework, PhishBench. With the addition of the ability to dynamically load features, metrics, and classifiers, our new and improved framework allows researchers to rapidly evaluate new features and methods for machine-learning based phishing detection. Researchers can compare under identical circumstances their contributions with numerous built-in features, ranking methods, and classifiers used in the literature with the right evaluation metrics. We will demonstrate PhishBench 2.0 and compare it against at least two other automated ML systems. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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