Towards Lightweight URL-Based Phishing Detection

Andrei Butnaru, Alexios Mylonas and Nikolaos Pitropakis
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Andrei Butnaru: School of Computing, Bournemouth University, Poole BH12 5BB, UK
Alexios Mylonas: Department of Computer Science, University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK
Nikolaos Pitropakis: Blockpass ID Lab, School of Computing Edinburgh Napier University, Edinburgh EH10 5DT, UK

Future Internet, 2021, vol. 13, issue 6, 1-15

Abstract: Nowadays, the majority of everyday computing devices, irrespective of their size and operating system, allow access to information and online services through web browsers. However, the pervasiveness of web browsing in our daily life does not come without security risks. This widespread practice of web browsing in combination with web users’ low situational awareness against cyber attacks, exposes them to a variety of threats, such as phishing, malware and profiling. Phishing attacks can compromise a target, individual or enterprise, through social interaction alone. Moreover, in the current threat landscape phishing attacks typically serve as an attack vector or initial step in a more complex campaign. To make matters worse, past work has demonstrated the inability of denylists, which are the default phishing countermeasure, to protect users from the dynamic nature of phishing URLs. In this context, our work uses supervised machine learning to block phishing attacks, based on a novel combination of features that are extracted solely from the URL. We evaluate our performance over time with a dataset which consists of active phishing attacks and compare it with Google Safe Browsing (GSB), i.e., the default security control in most popular web browsers. We find that our work outperforms GSB in all of our experiments, as well as performs well even against phishing URLs which are active one year after our model’s training.

Keywords: phishing; supervised machine learning; classifier; heuristics; URL-based; phishing (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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