Diverse Datasets and a Customizable Benchmarking Framework for Phishing
| Autor: | Shahryar Baki, Victor Zeng, Ayman El Aassal, Rakesh M. Verma, Luis F. T. Moraes, Avisha Das |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
media_common.quotation_subject Social engineering (security) 02 engineering and technology Benchmarking Phishing detection Deception Phishing Data science Test (assessment) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing media_common |
| Zdroj: | IWSPA@CODASPY |
| DOI: | 10.1145/3375708.3380313 |
| Popis: | Phishing is a challenging problem that has been addressed by many researchers in several papers using many different datatsets and techniques~\citedas2019sok. Researchers usually test their proposed methods with limited metrics, datasets, and parameters when presenting new features or approach(es). Hence, the need arises for a benchmarking framework and dataset to evaluate such systems as comprehensively as possible. In this paper, we discuss: (i) our efforts on the creation and dissemination of diverse and representative datasets for phishing email, website and URL detection, and (ii) PhishBench, our framework for benchmarking phishing detection systems. PhishBench allows researchers to evaluate and compare features and classification approaches easily and efficiently on the provided data. |
| Databáze: | OpenAIRE |
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