Abstract
Phishing – a hotbed of multibillion dollar underground economy – has become an important cybersecurity problem. The centralized blacklist approach used by most web browsers usually fails to detect zero-day attacks, leaving the ordinary users vulnerable to new phishing schemes; therefore, learning machine based approaches have been implemented for phishing detection. Many existing techniques in phishing website detection seem to include as many features as can be conceived, while identifying a relevant and representative subset of features to construct an accurate classifier remains an interesting issue in this particular application of machine learning. This paper evaluates correlation-based and wrapper feature selection techniques using real-world phishing data sets with 177 initial features. Experiments results show that applying an effective feature selection procedure generally results in statistically significant improvements in the classification accuracies of – among others – Naïve Bayes, Logistic Regression and Random Forests, in addition to improved efficiency in training time.
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References
APWG Phishing Activity Trends Report- 2nd Half (2010), http://apwg.org/reports/apwg_report_h2_2010.pdf (accessed on October 21, 2011)
Hall, M.A.: Correlation-based Feature Selection for Machine Learning. Hamilton, NewZealand (1999)
Kohavi, F., John, G.H.: Wrappers for Feature Subset Selection. Artificial Intelligence 97, 273–324 (1997)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley (1989)
Basnet, R., Mukkamala, S., Sung, A.H.: Detection of Phishing Attacks: A Machine Learning Approach. In: Prasad, B. (ed.) Soft Computing Applications in Industry. STUDFUZZ, vol. 226, pp. 373–383. Springer, Heidelberg (2008)
Ma, J., Saul, L.K., Safage, S., Voelker, G.M.: Beyond Blacklists: Learning to Detect Malicious Web Sites from Suspicious URLs. In: ACM SIGKDD, Paris, France, pp. 1245–1253 (2009)
Caruna, R., Freitag, D.: Greedy Attribute Selection. In: 11th International Conference in Machine Learning. Morgan Kaufmann, San Francisco (1994)
Zhang, Y., Hong, J., Cranor, L.: CANTINA: A Content-Based Approach to Detecting Phishing Web Sites. In: WWW 2007, Banff, Alberta, Canada. ACM Press (2007)
Whittaker, C., Ryner, B., Nazif, M.: Large-Scale Automatic Classification of Phishing Pages. In: 17th Annual Network and Distributed System Security Symposium, California, USA (2010)
Yahoo! Inc.: Random Link – random, http://random.yahoo.com/fast/ryl
PhishTank - Out of the Net, into the Tank, http://www.phishtank.com/developer_info.php
Garera, S., Provos, N., Chew, M., Rubin, A.D.: A Framework for Detection and Measurement of Phishing Attacks. In: 5th ACM Workshop on Recurring Malcode (WORM 2007), pp. 1–8. ACM Press, New York (2007)
PyLongURL - Python Library for LongURL.org, http://code.google.com/p/pylongurl/
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. ACM SIGKDD Explorations 11, 1–8 (2009)
Vafaie, H., Jong, K.D.: Robust Feature Selection Algorithms. In: International Conference on Tools with Artificial Intelligence (ICTAI), pp. 356–363 (1993)
Breiman, L.: Random Forests. Machine Learning 45, 5–32 (2001)
John, G., Langley, P.: Estimating Continuous Distributions in Bayesian Classifiers. In: 11th International Conference on Uncertainty in Artificial Intelligence, San Mateo, USA, pp. 338–345 (1995)
Toolan, F., Carthy, J.: Feature Selection for Spam and Phishing Detection: In: eCrime Researchers Summit (eCrime), Dallas, TX, pp. 1–9 (2010)
Miyamoto, D., Hazeyama, H., Kadobayashi, Y.: A Proposal of the AdaBoost-Based Detection of Phishing Sites. In: 2nd Joint Workshop on Information Security (2007)
Fette, I., Sadeh, N., Tomasic, A.: Learning to Detect Phishing Emails. In: 16th International Conference on World Wide Web, pp. 649–656 (2007)
Basnet, R.B., Sung, A.H.: Classifying Phishing Emails Using Confidence-Weighted Linear Classifiers. In: International Conference on Information Security and Artificial Intelligence, Chengdu, China, pp. 108–112 (2010)
Kittler, J.: Feature Set Search Algorithms. In: Chen, C.H. (ed.) Pattern Recognition and Signal Processing, The Netherlands (1978)
Miller, J.: Subset Selection in Regression. Chapman and Hall, New York (1990)
Quinlan, J.R.: C4.5 Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Basnet, R.B., Sung, A.H., Liu, Q.: Rule-Based Phishing Attack Detection. In: International Conference on Security and Management (SAM 2011), Las Vegas, NV (2011)
Holland, J.H.: Adaption in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Ludl, C., McAllister, S., Kirda, E., Kruegel, C.: On the Effectiveness of Techniques to Detect Phishing Sites. In: Hämmerli, B.M., Sommer, R. (eds.) DIMVA 2007. LNCS, vol. 4579, pp. 20–39. Springer, Heidelberg (2007)
le Cessie, S., van Houwelingen, J.C.: Ridge Estimators in Logistic Regression. Applied Statistics 41, 191–201 (1992)
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Basnet, R.B., Sung, A.H., Liu, Q. (2012). Feature Selection for Improved Phishing Detection. In: Jiang, H., Ding, W., Ali, M., Wu, X. (eds) Advanced Research in Applied Artificial Intelligence. IEA/AIE 2012. Lecture Notes in Computer Science(), vol 7345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31087-4_27
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DOI: https://doi.org/10.1007/978-3-642-31087-4_27
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