The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Identifying suspicious URLs: an application of large-scale online learning
Abstract
This paper explores online learning approaches for detecting malicious Web sites (those involved in criminal scams) using lexical and host-based features of the associated URLs. We show that this application is particularly appropriate for online algorithms as the size of the training data is larger than can be efficiently processed in batch and because the distribution of features that typify malicious URLs is changing continuously. Using a real-time system we developed for gathering URL features, combined with a real-time source of labeled URLs from a large Web mail provider, we demonstrate that recently-developed online algorithms can be as accurate as batch techniques, achieving classification accuracies up to 99% over a balanced data set.
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The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
Cited by
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
Stein, T., Chen, E., & Mangla, K. (2011) · Proceedings of the 4th Workshop on Social Network Systems · doi:10.1145/1989656.1989664 · Cited by 219
Chen, Z., & Liu, B. (2018) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00737ed1v01y201610aim033 · Cited by 154
(2012) · doi:10.1109/ecrime.2012.6489521 · Cited by 148
Metadata
- Authors
- Ma, J., Saul, L. K., Savage, S., & Voelker, G. M.
- Venue
- Proceedings of the 26th Annual International Conference on Machine Learning
- Publisher
- ACM
- Published
- 2009-06-14
- Pages
- 681-688
- OpenAlex
- W2121990650
- Cited by
- 498
- References
- 1
- FWCI
- 18.18
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Identifying suspicious URLs: an application of large-scale online learning. https://app.cochat.ai/papers/10.1145/1553374.1553462
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- Ma, J., Saul, L. K., Savage, S., & Voelker, G. M. (2009). Identifying suspicious URLs: an application of large-scale online learning. Proceedings of the 26th Annual International Conference on Machine Learning. https://doi.org/10.1145/1553374.1553462
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
