Machine learning based phishing detection from URLs

By Ozgur Koray Sahingoz, Ebubekir Buber, Önder Demir, Banu Diri. Expert systems with applications, 2019

749 citations recorded by the source corpus.

Abstract Due to the rapid growth of the Internet, users change their preference from traditional shopping to the electronic commerce. Instead of bank/shop robbery, nowadays, criminals try to find their victims in the cyberspace with some specific tricks. By using the anonymous structure of the Internet, attackers set out new techniques, such as phishing, to deceive victims with the use of false websites to collect their sensitive information such as account IDs, usernames, passwords, etc. Understanding whether a web page is legitimate or phishing is a very challenging problem, due to its semantics-based attack structure, which mainly exploits the computer users’ vulnerabilities. Although software companies launch new anti-phishing products, which use blacklists, heuristics, visual and machine learning-based approaches, these products cannot prevent all of the phishing attacks. In this paper, a real-time anti-phishing system, which uses seven different classification algorithms and natural language processing (NLP) based features, is proposed. The system has the following distinguishing properties from other studies in the literature: language independence, use of a huge size of phishing and legitimate data, real-time execution, detection of new websites, independence from third-party services and use of feature-rich classifiers. For measuring the performance of the system, a new dataset is constructed, and the experimental results are tested on it. According to the experimental and comparative results from the implemented classification algorithms, Random Forest algorithm with only NLP based features gives the best performance with the 97.98% accuracy rate for detection of phishing URLs.

Subfields: Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/8dd4a8eefa366b1b7d2471c1b8580df5bea23924

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Citations in the corpus

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research (2022, methodology): “[200] utilized LIME and EBM explanation techniques based on malicious URLs for a phishing experiment on a publicly available dataset Ebbu2017 [201].”
Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding (2019, methodology): “tional detection models are mainly based on content and URL information of the websites [23], [25], [26], while the detection framework here utilizes the transaction records between”
Malicious URL Detection using Machine Learning: A Survey (2017, methodology): “,71,82,86,95,113,131,133] [6,7,42,88,119,120,167,183,185,198,204] Logistic Regression [15,27,62,88,89,103,113,185,204] NaÃŕve Bayes [5,27,31,82,88,193,204] Decision Trees [5,19,27,31,119,157,185,204] [6,25,47,88,120,135,149] Others and Ensembles [13,40,54–56,171] Online Learning First-order algorith”