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Improving Auto-Detection of Phishing Websites using Fresh-Phish Framework

Improving Auto-Detection of Phishing Websites using Fresh-Phish Framework

Hossein Shirazi, Kyle Haefner, Indrakshi Ray
Copyright: © 2020 |Pages: 15
ISBN13: 9781799824602|ISBN10: 1799824608|EISBN13: 9781799824619
DOI: 10.4018/978-1-7998-2460-2.ch018
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MLA

Shirazi, Hossein, et al. "Improving Auto-Detection of Phishing Websites using Fresh-Phish Framework." Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 326-340. https://doi.org/10.4018/978-1-7998-2460-2.ch018

APA

Shirazi, H., Haefner, K., & Ray, I. (2020). Improving Auto-Detection of Phishing Websites using Fresh-Phish Framework. In I. Management Association (Ed.), Cognitive Analytics: Concepts, Methodologies, Tools, and Applications (pp. 326-340). IGI Global. https://doi.org/10.4018/978-1-7998-2460-2.ch018

Chicago

Shirazi, Hossein, Kyle Haefner, and Indrakshi Ray. "Improving Auto-Detection of Phishing Websites using Fresh-Phish Framework." In Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 326-340. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2460-2.ch018

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Abstract

Denizens of the Internet are under a barrage of phishing attacks of increasing frequency and sophistication. Emails accompanied by authentic looking websites are ensnaring users who, unwittingly, hand over their credentials compromising both their privacy and security. Methods such as the blacklisting of these phishing websites become untenable and cannot keep pace with the explosion of fake sites. Detection of nefarious websites must become automated and be able to adapt to this ever-evolving form of social engineering. There is an improved framework that was previously implemented called “Fresh-Phish”, for creating current machine-learning data for phishing websites. The improved framework uses a total of 28 different website features that query using python, then a large labeled dataset is built and analyze over several machine learning classifiers against this dataset to determine which is the most accurate. This modified framework improves the accuracy of modeling those features by using integer rather than binary values where possible. This article analyzes not just the accuracy of the technique, but also how long it takes to train the model.

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