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2019 | OriginalPaper | Chapter

Comparative Evaluation of Techniques for Detection of Phishing URLs

Authors : Oluwafemi Osho, Ayanfeoluwa Oluyomi, Sanjay Misra, Ravin Ahuja, Robertas Damasevicius, Rytis Maskeliunas

Published in: Applied Informatics

Publisher: Springer International Publishing

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Abstract

One of the popular cyberattacks today is phishing. It combines social engineering and online identity theft to delude Internet users into submitting their personal information to cybercriminals. Reports have shown continuous increase in the number and sophistication of this attack worldwide. Phishing Uniform Resource Locator (URL) is a malicious web address often created to look like legitimate URL, in order to deceive unsuspecting users. Many algorithms have been proposed to detect phishing URLs and classify them as benign or phishing. Most of these detection algorithms are based on machine learning and detect using inherent characteristics of the URLs. In this study, we examine the performance of a number of such techniques. The algorithms were tested using three publicly available datasets. Our results revealed, overall, the Random Forest algorithm as the best performing algorithm, achieving an accuracy of 97.3%.

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Literature
1.
Moghimi, M., Varjani, A.Y.: New rule-based phishing detection method. Expert Syst. Appl. 53, 231–242 (2016)CrossRef
2.
Mohammad, R., Thabtah, F., Mccluskey, L.: Predicting phishing websites based on self-structuring neural network. Neural Comput. Appl. 25(2), 443–458 (2014)CrossRef
3.
4.
Feroz, M.N., Mengel, S.: Phishing URL detection using URL ranking. In: IEEE International Congress on Big Data Phishing, pp. 635–638 (2015)
5.
Oluwafemi, O., Adesuyi, F.A., Abdulhamid, S.M.: Combating terrorism with cybersecurity: the nigerian perspective. World J. Comput. Appl. Technol. 1(4), 103–109 (2013)
6.
Garera, S., Provos, N., Chew, M., Rubin, A.D.: A framework for detection and measurement of phishing attacks. In: Proceedings of the 2007 ACM workshop on Recurring malcode - WORM 2007, pp. 1–8 (2007)
7.
Huang, H., Qian, L., Wang, Y.: A SVM-based technique to detect phishing URLs. Inf. Technol. J. 11(7), 921–925 (2012)CrossRef
8.
Abu-Nimeh, S., Nappa, D., Wang, X., Nair, S.: A comparison of machine learning techniques for phishing detection. In: Proceedings of the Anti-phishing Working Groups 2nd Annual eCrime Researchers Summit, pp. 60–69 (2007)
9.
Lee, J., Kim, D., Lee, C.-H.: Heuristic-based approach for phishing site detection using URL features. In: 3rd International Conference on Advances in Computing, Electronics and Electrical Technology - CEET 2015, pp. 131–135
10.
Jagatic, T.N., Johnson, N.A., Jakobsson, M., Menczer, F.: Social phishing. Commun. ACM 50(10), 94–100 (2007)CrossRef
11.
12.
Fu, A.Y., Wenyin, L., Deng, X.: Detecting phishing web pages with visual similarity assessment based on earth mover’s distance (EMD). IEEE Trans. Dependable Secure Comput. 3(4), 301–311 (2006)CrossRef
13.
Khonji, M., Iraqi, Y., Jones, A.: Lexical URL analysis for discriminating phishing and legitimate websites. In: Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference, pp. 109–115 (2011)
14.
Marchal, S., Saari, K., Singh, N., Asokan, N.: Know your phish: novel techniques for detecting phishing sites and their targets. In: Proceedings - International Conference on Distributed Computing Systems 2016, vol. 2016–August, no. Sect. V, pp. 323–333 (2016)
15.
Khonji, M., Iraqi, Y., Jones, A.: Phishing detection: a literature survey. IEEE Commun. Surv. Tutorials 15(4), 2091–2121 (2013)CrossRef
16.
Bergholz, A., Paaß, G., Reichartz, F., Strobel, S., Birlinghoven, S.: Improved phishing detection using model-based features. In: Fifth Conference on Email and Anti-spam, CEAS (2008)
17.
Khonji, M., Jones, A., Iraqi, Y.: A novel Phishing classification based on URL features. In: IEEE GCC Conference and Exhibition (GCC), pp. 221–224 (2011)
18.
Ma, J., Saul, L.K., Savage, S., Voelker, G.M.: Learning to detect malicious URLs. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 30 (2011)
19.
Miyamoto, D., Hazeyama, H., Kadobayashi, Y.: An evaluation of machine learning-based methods for detection of phishing sites. In: International Conference on Neural Information Processing, pp. 539–540 (2009)CrossRef
20.
Zhang, J., Wang, Y.: A real-time automatic detection of phishing URLs. In: 2nd International Conference on Computer Science and Network Technology (ICCSNT), pp. 1212–1216 (2012)
21.
Miyamoto, D., Hazeyama, H., Kadobayashi, Y.: An evaluation of machine learning-based methods for detection of phishing sites. In: International Conference on Neural Information Processing, pp. 539–546 (2008)CrossRef
22.
Abdulhamid, S.M., et al.: A review on mobile SMS spam filtering techniques. IEEE Access 5, 15650–15666 (2017)CrossRef
23.
Blanzieri, E., Bryl, A.: A survey of learning-based techniques of email spam filtering. Artif. Intell. Rev. 29(1), 63–92 (2008)CrossRef
24.
Panigrahi, P.: A comparative study of supervised machine learning techniques for spam E-mail filtering. In: Proceedings - 4th International Conference on Computational Intelligence and Communication Networks, CICN 2012, pp. 506–512 (2012)
25.
Abdulhamid, S.M., Shuaib, M., Osho, O.: Comparative analysis of classification algorithms for email spam detection. Int. J. Comput. Network Inf. Security 1, 60–67 (2018)CrossRef
26.
Iqbal, M., Abid, M.M., Ahmad, M., Khurshid, F.: Study on the effectiveness of spam detection technologies. Int. J. Inf. Technol. Comput. Sci. 01, 11–21 (2016)
27.
Aburrous, M., Hossain, M.A., Dahal, K., Thabtah, F.: Associative classification techniques for predicting e-banking phishing websites. In: International Conference on Multimedia Computing and Information Technology (MCIT), pp. 9–12 (2010)
28.
Aburrous, M., Hossain, M.A., Dahal, K., Thabtah, F.: Intelligent detection system for e-banking phishing websites using fuzzy data mining. Expert Syst. Appl. 37(12), 7913–7921 (2010)CrossRef
29.
Aburrous, M., Hossain, M.A., Dahal, K., Thabtah, F.: Predicting phishing websites using classification mining techniques with experimental case studies. In: Seventh International Conference on Information Technology: New Generations (ITNG), pp. 176–181 (2010)
30.
Ma, J., Saul, L.K., Savage, S., Voelker, G.M.: Beyond blacklists: learning to detect malicious web sites from suspicious URLs. In: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1245–1254 (2009)
31.
Basnet, R.B., Sung, A.H., Liu, Q.: Learning to detect phishing URLs. IJRET: Int. J. Res. Eng. Technol. 3(6), 11–24 (2014)CrossRef
32.
Gupta, R.: Comparison of classification algorithms to detect phishing web pages using feature selection and extraction. Int. J. Res. Granthaalayah 4(8), 118–135 (2016)
33.
Nawafleh, S., Hadi, W.: Multi-class associative classification to predicting phishing websites. Int. J. Acad. Res. 4(6), 302–306 (2012)CrossRef
34.
Ali, W.: Phishing website detection based on supervised machine learning with wrapper features selection. Int. J. Adv. Comput. Sci. Appl. 8(9), 72–78 (2017)
35.
Oluyomi, A., Osho, O., Shuaib, M.: Evaluation of classification algorithms for phishing URL detection. In: 2nd International Conference on Information and Communication Technology and Its Applications, pp. 243–249 (2018)
36.
37.
Metadata
Title
Comparative Evaluation of Techniques for Detection of Phishing URLs
Authors
Oluwafemi Osho
Ayanfeoluwa Oluyomi
Sanjay Misra
Ravin Ahuja
Robertas Damasevicius
Rytis Maskeliunas
Copyright Year
2019
Book
Applied Informatics

Print ISBN: 978-3-030-32474-2

Electronic ISBN: 978-3-030-32475-9

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-32475-9_28

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