Article

Learn to Detect Phishing Scams Using Learning and Ensemble ?Methods

Authors:

Alireza Saberi,

Mojtaba Vahidi,

Behrouz Minaei BidgoliAuthors Info & Claims

WI-IATW '07: Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops

Pages 311 - 314

Published: 02 November 2007 Publication History

Abstract

Phishing attack is a kind of identity theft which tries to steal ?confidential data like on?-?line bank account information?. In a ?phishing attack scenario, attacker deceives users by a fake email ?which is called scam. In this paper we employ three different ?learning methods to detect phishing scams. Then, we use ?ensemble methods on their results to improve our scam ?detection mechanism. Experimental results show that ?the proposed method can detect 94.4% of scam emails ?correctly, while only 0.08% of legitimate emails are ?classified as scams.

References

[1]

N. Chou, R. Ledesma, Y. Teraguchi and J.C. Mitchell, "Client-Side Defense against Web-Based Identity Theft", 11th Annual Network and Distributed System Security Symposium, San Diego, USA, February 2004.

[2]

R. Dhamija, J. D. Tygar, M. Hearst, "Why Phishing Works", CHI Conference on Human Factors in Computing Systems, Montreal, Canada, April 2006.

Digital Library

[3]

P. Pfleeger, S. Bloom, "Canning Spam: Proposed Solutions to Unwanted Email", Security & Privacy Magazine, IEEE, vol. 3, no. 2, March-April 2005, pp. 40-7.

Digital Library

[4]

M. Chandrasekaran, K. Karayanan, S. Upadhyaya. "Towards phishing e-mail detection based on their structural properties", New York State Cyber Security Conference, USA, 2006.

[5]

M. Sahami, S. Dumais, D. Heckerman, E. Horvitz, "A bayesian approach to filtering junk e-mail", AAAI Workshop on Learning for Text Categorization, Madison, Wisconsin, USA, July 1998.

[6]

E. Airoldi, B. Malin, "Data Mining Challenges For Electronic Safety: The Case of Fraudulent Intent Detection in E-Mails", In proceedings of the IEEE Workshop on Privacy and Security Aspects of Data Mining, 2004, pp. 57-66.

[7]

I. Fette, N. Sadeh, A. Tomasic, "Learning to Detect Phishing Emails", Proceedings of the 16th international conference on World Wide Web, Banff, Alberta, Canada 2007.

Digital Library

[8]

B. V. Dasarathy. "Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques". IEEE Computer Society Press, Los Alamitos, California, 1990.

[9]

E. Airoldi and W. Cohen. "Bayesian models for frequent terms in text", Technical Report No. CMU-CALD-04-106, Carnegie Mellon University, July 2004.

[10]

E. Day, "Consensus methods as tools for data analysis", In H. Bock, editor, Classification and Related Methods for Data Analysis, Elsevier Science Publishers B.V. (North Holland), 1988, pp. 317-324.

[11]

N. Littlestone and M. Warmuth, "Weighted majority algorithm", Information and Computation, vol. 108, 1994, pp. 212-261.

Digital Library

[12]

P. Domingos and M. Pazzani. "On the optimality of the simple Bayesian classifier under zero-one loss" Machine Learning, 29:103-130, 1997

Digital Library

[13]

Y. S. Huang and C. Y. Suen. "A method of combining multiple experts for the recognition of unconstrained handwritten numerals." IEEE Transactions on Pattern Analysis and Machine Intelligence, 17:90-93, 1995.

Digital Library

[14]

S. Hershkop, S. Stolfo, "Combining email models for false positive reduction", Proceeding of the eleventh ACM international conference on Knowledge discovery in data mining, Chicago, Illinois, USA, 2005.

Digital Library

[15]

V. Metsis, I. Androutsopoulos, G. Paliouras, "Spam Filtering with Naive Bayes - Which Naive Bayes?", Third Conference on Email and AntiSpam, Mountain View, California, USA, July, 2006.

[16]

The Internet Defence Phishery. Repository of phishing emails, September 2006 http://phishery.internetdefence.net/data/

Cited By

Sharma KQian FJiang HRuchansky NZhang MLiu Y(2019)Combating Fake NewsACM Transactions on Intelligent Systems and Technology10.1145/330526010:3(1-42)Online publication date: 12-Apr-2019
https://dl.acm.org/doi/10.1145/3305260
Anandita Yadav DPaliwal PKumar DTripathi R(2017)A Novel Ensemble Based Identification of Phishing E-MailsProceedings of the 9th International Conference on Machine Learning and Computing10.1145/3055635.3056583(447-451)Online publication date: 24-Feb-2017
https://dl.acm.org/doi/10.1145/3055635.3056583
Aleroud AZhou L(2017)Phishing environments, techniques, and countermeasuresComputers and Security10.1016/j.cose.2017.04.00668:C(160-196)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1016/j.cose.2017.04.006
Show More Cited By

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Learn to Detect Phishing Scams Using Learning and Ensemble ?Methods

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Published In

cover image ACM Conferences

WI-IATW '07: Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops

November 2007

513 pages

ISBN:0769530281

Sponsors

SIGAI: ACM Special Interest Group on Artificial Intelligence

Publisher

IEEE Computer Society

United States

Publication History

Published: 02 November 2007

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Lerning MethodsPhishingScamSpam.

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Cited By

Sharma KQian FJiang HRuchansky NZhang MLiu Y(2019)Combating Fake NewsACM Transactions on Intelligent Systems and Technology10.1145/330526010:3(1-42)Online publication date: 12-Apr-2019
https://dl.acm.org/doi/10.1145/3305260
Anandita Yadav DPaliwal PKumar DTripathi R(2017)A Novel Ensemble Based Identification of Phishing E-MailsProceedings of the 9th International Conference on Machine Learning and Computing10.1145/3055635.3056583(447-451)Online publication date: 24-Feb-2017
https://dl.acm.org/doi/10.1145/3055635.3056583
Aleroud AZhou L(2017)Phishing environments, techniques, and countermeasuresComputers and Security10.1016/j.cose.2017.04.00668:C(160-196)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1016/j.cose.2017.04.006
Parvin HMinaei BAlizadeh H(2011)A heuristic classifier ensemble for huge datasetsProceedings of the 7th international conference on Active media technology10.5555/2033896.2033905(29-38)Online publication date: 7-Sep-2011
https://dl.acm.org/doi/10.5555/2033896.2033905
Parvin HMinaei BBeigi AHelmi H(2011)Classification ensemble by genetic algorithmsProceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I10.5555/1997052.1997095(391-399)Online publication date: 14-Apr-2011
https://dl.acm.org/doi/10.5555/1997052.1997095
Parvin HMinaei-Bidgoli BParvin S(2011)A scalable heuristic classifier for huge datasetsProceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-642-25085-9_45(380-390)Online publication date: 15-Nov-2011
https://dl.acm.org/doi/10.1007/978-3-642-25085-9_45

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