PhishGILLNET-phishing detection methodology using probabilistic latent semantic analysis, AdaBoost, and co-training

Eurasip Journal on Information Security

Volumn 2012, Issue , 2012, Pages

  Ramanathan, Venkatesh ^a   Wechsler, Harry ^a  

^a Krasnow Institute for Advanced Study   (United States)

Author keywords

Boosting; Co training; Identity theft; Machine learning; Natural language processing; Phishing; Probabilistic latent semantic analysis

Indexed keywords

ADAPTIVE BOOSTING; CLASSIFICATION (OF INFORMATION); CRIME; ELECTRONIC MAIL; LEARNING SYSTEMS; NATURAL LANGUAGE PROCESSING SYSTEMS; PROBABILITY DISTRIBUTIONS; PROFITABILITY; SEMANTICS;

BOOSTING; CO-TRAINING; IDENTITY THEFT; NATURAL LANGUAGE PROCESSING; PHISHING; PROBABILISTIC LATENT SEMANTIC ANALYSIS;

COMPUTER CRIME;

EID: 84874664509     PISSN: 16874161     EISSN: 2510523X     Source Type: Journal    
DOI: 10.1186/1687-417X-2012-1     Document Type: Article

References (74)

1. National Data-Deter. Detect. Defend. Avoid ID Theft.http://www.ftc.gov/ bcp/edu/microsites/idtheft/reference-desk/national-data.html. Accessed 21 July 2011
2. Google Says Phishers Stole E-mail From US Officials, Others, PCWorld Business Center.http://www.pcworld.com/businesscenter/article/229202/google- says-phishers-stole-email-from-us-officials-others.html. Accessed 21 July 2011
3. DNSBL Information-Spam Database Lookup.http://www.dnsbl.info/. Accessed 21 July 2011
4. Snort-Home Page.http://www.snort.org/. Accessed 21 July 2011
5. H Kim, JH Huh, Detecting DNS-poisoning-based phishing attacks from their network performance characteristics. Electron Lett. 47(11):656-658 (2011). doi:10.1049/el.2011.0399
6. ID Sender, http://www.microsoft.com/mscorp/safety/technologies/senderid/ default.mspx. Accessed 21 July 2011
7. DomainKey Library and Implementor's Tools.http://domainkeys.sourceforge. net/. Accessed 21 July 2011
8. H Kim, P Howland, H Park, Dimension reduction in text classification with support vector machines. J Mach Learn Res. 6, 37-53 (2005)
9. SpamAssassin: Welcome to SpamAssassin.http://spamassassin.apache.org/. Accessed 19 July 2011
10. I Fette, N Sadeh, A Tomasic, Learning to detect phishing emails. in Proceedings of the 16th international conference on World Wide Web, vol. 1. (Banff, AB, Canada, 2007), pp. 649-656 (Pubitemid 47582294)
11. S Abu-Nimeh, D Nappa, X Wang, S Nair, A comparison of machine learning techniques for phishing detection. in Proceedings of the eCrime Researchers Summit, vol. 1. (Pittsburgh, PA, USA, 2007), pp. 60-69
12. S Abu-Nimeh, D Nappa, X Wang, S Nair, Distributed phishing detection by applying variable selection using Bayesian additive regression trees. in IEEE International Conference on Communications, vol. 1. (Dresden, Germany, 2009), pp. 1-5
13. D Miyamoto, H Hazeyama, Y Kadobayashi, An evaluation of machine learning-based methods for detection of phishing sites. in Proceedings of the 15th International Conference on Advances in Neuro-Information Processing (Springer-Verlag, Heidelberg, 2009), vol. 1. (Auckland, New Zealand, 2008), pp. 539-546
14. F Toolan, J Carthy, Phishing Detection using Classifier Ensembles. (eCrime Researchers Summit, Tacoma, WA, USA, 2009)
15. WN Gansterer, D Pölz, E-mail classification for phishing defense. in Proceedings of the 31st European Conference on IR Research on Advances in Information Retrieval, (Springer-Verlag, Heidelberg, 2009), vol. 1. (Toulouse, France, 2009), pp. 449-460
16. A Bergholz, J-H Chang, G Paaß, F Reichartz, S Strobel, Improved phishing detection using model-based features. in Proceedings of the Conference on Email and Anti-Spam (CEAS), vol. 1. (Mountain View, California, USA, 2008), pp. 1-10
17. A Bergholz, JD Beer, S Glahn, MF Moens, G Paaß, S Strobel, New filtering approaches for phishing email. J Comput Secur. 18(1):7-35 (2010)
18. A Bergholz, G Paaß, L D'Addona, D Dato, A real-life study in phishing detection. in Proceedings of the Conference on Email and Anti-Spam (CEAS), vol. 1. (Redmond, Washington, USA, 2010), pp. 1-10
19. F Toolan, J Carthy, Feature Selection for Spam and Phishing Detection. in eCrime Researchers Summit, vol. 1. (Dallas, Texas, USA, 2010), pp. 1-12
20. M Khonji, A Jones, Y Iraqi, A Study of Feature Subset Evaluators and Feature Subset Searching Methods for Phishing Classification. (CEAS'11, Perth, Australia, 2011)
21. AAD Al-Momani, TC Wan, K Al-Saedi, A Altaher, S Ramadass, A Manasrah, LB Melhim, M Anbar, An online model on evolving phishing e-mail detection and classification method. J Appl Sci. 11(18):3301-3307 (2011). doi:10.3923/jas. 2011.3301.3307
22. J Zhan, L Thomas, Phishing detection using stochastic learning based weak estimators. in IEEE Symposium on Computation Intelligence in Cyber Security, vol. 1. (Paris, France, 2011), pp. 55-59
23. J Yearwood, M Mammadov, D Webb, Profiling phishing activity based on hyperlinks extracted from phishing emails. J Social Netw Anal Mining. 2(1):5-16 (2011)
24. G Xiang, J Hong, CP Rose, L Cranor, A feature-rich machine learning framework for detecting phishing web sites. ACM Trans Inf Syst Secur 14(2):1-28 (2011). Article 21
25. M Khonji, Y Iraqi, A Jones, Lexical URL analysis for discriminating phishing and legitimate websites. in Proceedings of 8th Annual Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference, vol. 1. (Perth, Australia, 2011), pp. 109-115
26. H Zhang, G Liu, TWS Chow, W Liu, Textual and visual content-based anti-phishing: a Bayesian approach. IEEE Trans Neural Netw. 22(10):1532-1546 (2011)
27. CH Hsu, P Wang, S Pu, Identify fixed-path phishing attack by STC. in Proceedings of 8th Annual Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference, vol. 1. (Perth, Australia, 2011), pp. 172-175
28. M Khonji, A Jones, Y Iraqi, A novel phishing classification based on URL features. in IEEE GCC Conference and Exhibition, vol. 1. (Dubai, UAE, 2011), pp. 221-224
29. L Wenyin, G Liu, B Qiu, X Quan, Anti-phishing through phishing target discovery. IEEE J Internet Comput. 16(2):52-61 (2011)
30. SpoofGuard.http://crypto.stanford.edu/SpoofGuard/. Accessed 21 July 2011
31. A Cordero, T Blain, Catching phish: detecting phishing attacks from rendered website images.http://citeseerx.ist.psu.edu/viewdoc/download? doi=10.1.1.92.9084&rep=rep1&type=pdf. Accessed 26 July 2011
32. CallingID-Your Protection from Identity Theft, Fraud, Scams and Malware. http://www.callingid.com/Default.aspx. Accessed 21 July 2011
33. CloudMark.http://www.cloudmark.com/en/products/cloudmark-desktopone/ index. Accessed 26 July 2011
34. Netcraft Anti-Phishing Toolbar.http://toolbar.netcraft.com/. Accessed 26 July 2011
35. FirePhish, https://addons.mozilla.org/en-US/firefox/addon/firephish-anti- phishing-extens/. Accessed 26 July 2011
36. eBay Toolbar.http://download.cnet.com/eBay-Toolbar/3000-12512-4-10153544. html?tag=contentMain;downloadLinks. Accessed 21 July 2011
37. IE Phishing Filter.http://support.microsoft.com/kb/930168. Accessed 21 July 2011
38. S Ab-Nimeh, S Nair, Bypassing security toolbars and phishing filters via DNS poisoning. in IEEE Global Telecommunications Conference, vol. 1. (New Orleans, Louisiana, USA, 2008), pp. 1-6
39. A Jain, V Richariya, Implementing a web browser with phishing detection techniques. World Comput Sci Inf Technol J. 1(7):28-291 (2011)
40. E Lin, S Greenberg, E Trotter, D Ma, J Aycok, Does domain highlighting help people identify phishing sites? CHI 2011. Vancouver, BC, Canada http://survey.mailfrontier.com/survey/phishing-uk.html (2011). Accessed 21 July 2011
41. Y Chen, F Zahedi, A Abbasi, Interface design elements for anti-phishing systems. in Proc of the 6th International Conference on Service-Oriented Perspectives in Design Science Research (Springer-Verlag, Berlin, 2011), vol. 1. (Milwaukee, Wisconsin, USA, 2011), pp. 253-265
42. AP Felt, D Wagner, Phishing on mobile devices. Web 2.0 Security and Privacy Workshop. (2011)
43. MailFrontier Phishing IQ Test-UK Edition.http://survey.mailfrontier.com/ survey/phishing-uk.html. Accessed 21 July 2011
44. SA Robila, JW Ragucci, Don't be a phish: steps in user education. in Proceedings of the 11th annual SIGCSE conference on Innovation and technology in computer science education, vol. 1. (Bologna, Italy, 2006), pp. 237-241
45. TN Jagatic, NA Johnson, M Jakobsson, F Menczer, Social phishing Commun ACM. 50, 94-100 (2007)
46. NAG Arachchilage, M Cole, Design a mobile game for home computer users to prevent from phishing attacks. in IEEE International Conference on Information Society, vol. 1. (London, UK, 2011), pp. 485-489
47. SS Tseng, KY Chen, TJ Lee, JY Weng, Automatic content generation for anti-phishing education game. in IEEE International Conference on Electrical and Control Engineering, vol. 1. (Yichang, China, 2011), pp. 6390-6394
48. T Moore, R Clayton, The impact of public information on phishing attack and defense. Commun Strat. 81(1):45-68 (2011)
49. Gillnet-Wikipedia.http://en.wikipedia.org/wiki/Gillnet. Accessed 25 July 2011
50. The Porter Stemming Algorithm.http://tartarus.org/~martin/PorterStemmer/. Accessed 26 July 2011
51. WordNet.http://wordnet.princeton.edu/. Accessed 26 July 2011
52. Google Suggest API.http://code.google.com/p/google-refine/wiki/ SuggestApi. Accessed 26 July 2011
53. Levenshtein distance.http://en.wikipedia.org/wiki/Levenshtein-distance. Accessed 26 July 2011
54. T Hofmann, Probabilistic latent semantic indexing. in Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, vol. 1. (Berkeley, California, USA, 1999), pp. 50-57
55. T Hofmann, Unsupervised learning by probabilistic latent semantic analysis. Mach Learn. 42, 177-196 (2001). doi:10.1023/A:1007617005950 (Pubitemid 32872403)
56. Y Freund, R Schapire, A short introduction to boosting. J Jpn Soc Artif Intell. 14, 771-780 (1999)
57. R Quinlan, C4.5: Programs for Machine Learning. (Morgan Kaufmann Publishers, San Mateo, CA, 1993)
58. WW Cohen, Fast effective rule induction. in 12th International Conference on Machine Learning, vol. 1. (Tahoe City, California, USA, 1995), pp. 115-123
59. L Breiman, Random forests. Mach Learn. 45(1):5-32 (2001). doi:10.1023/A:1010933404324 (Pubitemid 32933532)
60. V Vapnik, The Nature of Statistical Learning Theory. (Springer, New York, 1995)
61. M Sumner, E Frank, M Gall, Speeding up logistic model tree induction. in 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (Springer LNCS), vol. 3721. (Porto, Portugal, 2005), pp. 675-683
62. A Blum, T Mitchell, Combining labeled and unlabeled data with co-training. in Proceedings of the Workshop on Computation Learning Theory, (ACM), vol. 1. (Madison, Wisconsin, USA, 1998), pp. 92-100
63. K Nigam, R Ghani, Analyzing the effectiveness and applicability of co-training. in International Conference on Information and Knowledge Management, (ACM), vol. 1. (McLean, Virginia, USA, 2000), pp. 86-93
64. S Kiritchenko, S Matwin, Email classification with co-training. in Proceedings of CASCON, (ACM), vol. 1. (Toronto, Ontario, Canada, 2001), pp. 1-8
65. J Chan, I Koprinska, J Poon, Co-training with a single natural feature set applied to email classification. in Proceedings of the International Conference on Web Intelligence, vol. 1. (Beijing, China, 2004), pp. 586-589 (Pubitemid 40401170)
66. X Wan, Co-training for cross-lingual sentiment classification. in Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, vol. 1. (Suntec, Singapore, 2009), pp. 235-243
67. A Kumar, H Daumé III, A co-training approach for multi-view spectral clustering. in Proceedings of the 28th International Conference on Machine Learning, ACM, vol. 1. (Bellevue, WA, USA, 2011), pp. 393-400
68. SpamAssassin Publiccorpus.http://spamassassin.apache.org/publiccorpus/. Accessed 21 July 2011
69. PhishingCorpus.http://monkey.org/~jose/wiki/doku.php. Accessed 21 July 2011
70. Enron Email Dataset.http://www.cs.cmu.edu/~enron/. Accessed November 2011
71. SPAM Archive.http://untroubled.org/spam/. Accessed November 2011
72. PhishTank.http://www.phishtank.com. Accessed November 2011
73. SURBL URI Reputation Data.http://www.surbl.org. Accessed November 2011
74. Internet Security Threat Reports.http://www.symantec.com/business/ threatreport/archive.jsp. Accessed November 2011.