The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Beyond blacklists: learning to detect malicious web sites from suspicious URLs
Abstract
Malicious Web sites are a cornerstone of Internet criminal activities. As a result, there has been broad interest in developing systems to prevent the end user from visiting such sites. In this paper, we describe an approach to this problem based on automated URL classification, using statistical methods to discover the tell-tale lexical and host-based properties of malicious Web site URLs. These methods are able to learn highly predictive models by extracting and automatically analyzing tens of thousands of features potentially indicative of suspicious URLs. The resulting classifiers obtain 95-99% accuracy, detecting large numbers of malicious Web sites from their URLs, with only modest false positives.
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Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
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Cantina: a content-based approach to detecting phishing web sites
Zhang, Y., Hong, J. I., & Cranor, L. F. (2007) · Proceedings of the 16th international conference on World Wide Web · doi:10.1145/1242572.1242659 · Cited by 757
Learning to detect phishing emails
Fette, I., Sadeh, N., & Tomasic, A. (2007) · Proceedings of the 16th international conference on World Wide Web · doi:10.1145/1242572.1242660 · Cited by 604
A comparison of machine learning techniques for phishing detection
Abu-Nimeh, S., Nappa, D., Wang, X., & Nair, S. (2007) · Proceedings of the anti-phishing working groups 2nd annual eCrime researchers summit · doi:10.1145/1299015.1299021 · Cited by 502
Identifying suspicious URLs: an application of large-scale online learning
Ma, J., Saul, L. K., Savage, S., & Voelker, G. M. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553462 · Cited by 498
Self-citationA framework for detection and measurement of phishing attacks
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Cited by
Graph based anomaly detection and description: a survey
Akoglu, L., Tong, H., & Koutra, D. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0365-y · Cited by 1,504
Detecting algorithmically generated malicious domain names
Yadav, S., Reddy, A. K. K., Reddy, A. L. N., & Ranjan, S. (2010) · Proceedings of the 10th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1879141.1879148 · Cited by 355
PhishStorm: Detecting Phishing With Streaming Analytics
Marchal, S., Francois, J., State, R., & Engel, T. (2014) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2014.2377295 · Cited by 271
FENCE: Feasible Evasion Attacks on Neural Networks in Constrained Environments
Chernikova, A., & Oprea, A. (2022) · ACM Transactions on Privacy and Security · doi:10.1145/3544746 · Cited by 52
Malicious URL Detection Using Transformers’ NLP Models and Machine Learning
Niyaoui, O., & Reda, O. M. (2024) · Lecture Notes in Networks and Systems · doi:10.1007/978-3-031-54318-0_35 · Cited by 3
Metadata
- Authors
- Ma, J., Saul, L. K., Savage, S., & Voelker, G. M.
- Venue
- Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2009-06-28
- Pages
- 1245-1254
- OpenAlex
- W2146729596
- Cited by
- 803
- References
- 22
- FWCI
- 22.66
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Ma, J., Saul, L. K., Savage, S., & Voelker, G. M. (2009). Beyond blacklists: learning to detect malicious web sites from suspicious URLs. Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/1557019.1557153
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