Towards Lightweight URL-Based Phishing Detection
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-06-01
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| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/13/6/154 |
| _version_ | 1797530301339009024 |
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| author | Andrei Butnaru Alexios Mylonas Nikolaos Pitropakis |
| author_facet | Andrei Butnaru Alexios Mylonas Nikolaos Pitropakis |
| author_sort | Andrei Butnaru |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T10:27:06Z |
| format | Article |
| id | doaj.art-2f9ee233d1ee41738153e829ebc8445d |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-10T10:27:06Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-2f9ee233d1ee41738153e829ebc8445d2023-11-21T23:56:26ZengMDPI AGFuture Internet1999-59032021-06-0113615410.3390/fi13060154Towards Lightweight URL-Based Phishing DetectionAndrei Butnaru0Alexios Mylonas1Nikolaos Pitropakis2School of Computing, Bournemouth University, Poole BH12 5BB, UKDepartment of Computer Science, University of Hertfordshire, College Lane, Hatfield AL10 9AB, UKBlockpass ID Lab, School of Computing Edinburgh Napier University, Edinburgh EH10 5DT, UKNowadays, 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.https://www.mdpi.com/1999-5903/13/6/154phishingsupervised machine learningclassifierheuristicsURL-basedphishing |
| spellingShingle | Andrei Butnaru Alexios Mylonas Nikolaos Pitropakis Towards Lightweight URL-Based Phishing Detection Future Internet phishing supervised machine learning classifier heuristics URL-based phishing |
| title | Towards Lightweight URL-Based Phishing Detection |
| title_full | Towards Lightweight URL-Based Phishing Detection |
| title_fullStr | Towards Lightweight URL-Based Phishing Detection |
| title_full_unstemmed | Towards Lightweight URL-Based Phishing Detection |
| title_short | Towards Lightweight URL-Based Phishing Detection |
| title_sort | towards lightweight url based phishing detection |
| topic | phishing supervised machine learning classifier heuristics URL-based phishing |
| url | https://www.mdpi.com/1999-5903/13/6/154 |
| work_keys_str_mv | AT andreibutnaru towardslightweighturlbasedphishingdetection AT alexiosmylonas towardslightweighturlbasedphishingdetection AT nikolaospitropakis towardslightweighturlbasedphishingdetection |