The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A comparison of machine learning techniques for phishing detection
Abstract
There are many applications available for phishing detection. However, unlike predicting spam, there are only few studies that compare machine learning techniques in predicting phishing. The present study compares the predictive accuracy of several machine learning methods including Logistic Regression (LR), Classification and Regression Trees (CART), Bayesian Additive Regression Trees (BART), Support Vector Machines (SVM), Random Forests (RF), and Neural Networks (NNet) for predicting phishing emails. A data set of 2889 phishing and legitimate emails is used in the comparative study. In addition, 43 features are used to train and test the classifiers.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Androutsopoulos, I., Koutsias, J., Chandrinos, K. V., & Spyropoulos, C. D. (2000) · Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/345508.345569 · Cited by 465
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Beyond blacklists: learning to detect malicious web sites from suspicious URLs
Ma, J., Saul, L. K., Savage, S., & Voelker, G. M. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557153 · Cited by 803
Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
Crothers, E., Japkowicz, N., & Viktor, H. (2022) · arXiv · doi:10.48550/arxiv.2210.07321 · Cited by 196
“Mitigation of NLG-enabled phishing attacks will be similar to established work on existing phishing attacks, including both automated detection systems, user reporting, and awareness campaigns [1].”
Machine-Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
Crothers, E. N., Japkowicz, N., & Viktor, H. L. (2023) · IEEE Access · doi:10.1109/access.2023.3294090 · Cited by 196
“Mitigation of NLG-enabled phishing attacks will be similar to established work on existing phishing attacks, including both automated detection systems, user reporting, and awareness campaigns [1].”
A survey of phishing attack techniques, defence mechanisms and open research challenges
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Metadata
- Authors
- Abu-Nimeh, S., Nappa, D., Wang, X., & Nair, S.
- Venue
- Proceedings of the anti-phishing working groups 2nd annual eCrime researchers summit
- Publisher
- ACM
- Published
- 2007-10-04
- Pages
- 60-69
- OpenAlex
- W2002964284
- Cited by
- 502
- References
- 2
- FWCI
- 8.24
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Abu-Nimeh, S., Nappa, D., Wang, X., & Nair, S. (2007). A comparison of machine learning techniques for phishing detection. Proceedings of the anti-phishing working groups 2nd annual eCrime researchers summit. https://doi.org/10.1145/1299015.1299021
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