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An intelligent cyber security phishing detection system using deep learning techniques

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Abstract

Recently, phishing attacks have become one of the most prominent social engineering attacks faced by public internet users, governments, and businesses. In response to this threat, this paper proposes to give a complete vision to what Machine learning is, what phishers are using to trick gullible users with different types of phishing attacks techniques and based on our survey that phishing emails is the most effective on the targeted sectors and users which we are going to compare as well. Therefore, more effective phishing detection technology is needed to curb the threat of phishing emails that are growing at an alarming rate in recent years, thus will discuss the techniques of mitigation of phishing by Machine learning algorithms and technical solutions that have been proposed to mitigate the problem of phishing and valuable awareness knowledge users should be aware to detect and prevent from being duped by phishing scams. In this work, we proposed a detection model using machine learning techniques by splitting the dataset to train the detection model and validating the results using the test data , to capture inherent characteristics of the email text, and other features to be classified as phishing or non-phishing using three different data sets, After making a comparison between them, we obtained that the most number of features used the most accurate and efficient results achieved. the best ML algorithm accuracy were 0.88, 1.00, and 0.97 consecutively for boosted decision tree on the applied data sets.

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Data availability

The data set used in the work will be available upon request.

References

  1. Al-Masalha, H., Hnaif, A.A., Kanan, T.: Cyber-crime effect on Jordanian society. Int. J. Adv. Soft Comput. Appl. 12(3), 123–139 (2020)

    Google Scholar 

  2. Saini, B., Srivastava, S., Bajpai, A.: Deep CNN model for nanotoxicity classification using microscopic images. Int. J. Adv. Soft Comput. Appl. 12(2), 22 (2020)

    Google Scholar 

  3. Al-Zubi, S., Aqel, D., Lafi, M.: An intelligent system for blood donation process optimization-smart techniques for minimizing blood wastages. Clust. Comput. 2022, 1–11 (2022). https://doi.org/10.1007/s10586-022-03594-3

    Article  Google Scholar 

  4. Aqel, D., Al-Zubi, S., Mughaid, A., Jararweh, Y.: Extreme learning machine for plant diseases classification: a sustainable approach for smart agriculture. Clust. Comput. 2021, 1–14 (2021). https://doi.org/10.1007/s10586-021-03397-y

    Article  Google Scholar 

  5. Srivastava, S., Singh, A.K.: Fraud detection in the distributed graph database. Clust. Comput. 2022, 1–23 (2022). https://doi.org/10.1007/s10586-022-03540-3

    Article  Google Scholar 

  6. Kim, D., Kim, Y.-H., Shin, D., Shin, D.: Fast attack detection system using log analysis and attack tree generation. Clust. Comput. 22(1), 1827–1835 (2019)

    Article  Google Scholar 

  7. Aldabbas, H., Amin, R.: A novel mechanism to handle address spoofing attacks in sdn based iot. Clust. Comput. 24(4), 3011–3026 (2021)

    Article  Google Scholar 

  8. Abusukhon, A., AlZu’bi, S.: New direction of cryptography: a review on text-to-image encryption algorithms based on rgb color value. In: Proceedings of the 2020 Seventh International Conference on Software Defined Systems (SDS), pp. 235–239. IEEE (2020)

  9. Obeidat, I., Mughaid, A., Alzoubi, S.: A secure encrypted protocol for clients’ handshaking in the same network. Int. J. Interact. Mob. Technol. 13, 47–57 (2019)

    Article  Google Scholar 

  10. Salahdine, F., Kaabouch, N.: Social engineering attacks: a survey. Future Internet 11(4), 89 (2019)

    Article  Google Scholar 

  11. Khonji, M., Iraqi, Y., Jones, A.: Phishing detection: a literature survey. IEEE Commun. Surv. Tutor. 15(4), 2091–2121 (2013)

    Article  Google Scholar 

  12. Whittaker, C., Ryner, B., Nazif, M.: Large-scale automatic classification of phishing pages. In: Proceedings of the Network and Distributed System Security Symposium (2010)

  13. Hong, J.: The state of phishing attacks. Commun. ACM 55(1), 74–81 (2012)

    Article  Google Scholar 

  14. Maqableh, M., Alia, M.: Evaluation online learning of undergraduate students under lockdown amidst covid-19 pandemic: the online learning experience and students’ satisfaction. Child Youth Serv. Rev. 128, 106160 (2021)

    Article  Google Scholar 

  15. Zhao, W., Zhu, Y.: An email classification scheme based on decision-theoretic rough set theory and analysis of email security. In: Proceedings of the TENCON 2005-2005 IEEE Region 10 Conference, pp. 1–6. IEEE (2005)

  16. Vinayakumar, R., Soman, K., Poornachandran, P., Akarsh, S., Elhoseny, M.: Deep learning framework for cyber threat situational awareness based on email and url data analysis. In: Hassanien, A.E., Elhoseny, M. (eds.) Cybersecurity and Secure Information Systems, pp. 87–124. Springer, New York (2019)

    Chapter  Google Scholar 

  17. AlZu’bi, S., Al-Qatawneh, S., Alsmirat, M.: Transferable hmm trained matrices for accelerating statistical segmentation time. In: Proceedings of the 2018 Fifth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 172–176. IEEE (2018)

  18. Al-Zubi, S., Hawashin, B., Mughaid, A., Baker, T.: Efficient 3d medical image segmentation algorithm over a secured multimedia network. Multimed. Tools Appl. 80(11), 16887–16905 (2021)

    Article  Google Scholar 

  19. AlZu’bi, S., Jararweh, Y.: Data fusion in autonomous vehicles research, literature tracing from imaginary idea to smart surrounding community. In: Proceedings of the 2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC), pp. 306–311. IEEE (2020)

  20. AlKhatib, A.A., Sawalha, T., AlZu’bi, S.: Load balancing techniques in software-defined cloud computing: an overview. In: Proceedings of the 2020 Seventh International Conference on Software Defined Systems (SDS), pp. 240–244. IEEE (2020)

  21. Fette, I., Sadeh, N., Tomasic, A.: Learning to detect phishing emails. In: Proceedings of the 16th international conference on World Wide Web, pp. 649–656 (2007)

  22. Bhat, V.H., Malkani, V.R., Shenoy, P.D., Venugopal, K., Patnaik, L.: Classification of email using beaks: behavior and keyword stemming. In: Proceedings of the TENCON 2011-2011 IEEE Region 10 Conference, pp. 1139–1143. IEEE (2011)

  23. Form, L.M., Chiew, K.L., Tiong, W.K.: Phishing email detection technique by using hybrid features. In: Proceedings of the 2015 9th International Conference on IT in Asia (CITA), pp. 1–5. IEEE (2015)

  24. Elbes, M., Alrawashdeh, T., Almaita, E., AlZu’bi, S., Jararweh, Y.: A platform for power management based on indoor localization in smart buildings using long short-term neural networks’’. Trans. Emerg. Telecommun. Technol. 33, e3867 (2020)

    Google Scholar 

  25. AlZu’bi, S., Shehab, M.A., Al-Ayyoub, M., Benkhelifa, E., Jararweh, Y.: Parallel implementation of fcm-based volume segmentation of 3d images. In: Proceedings of the IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), 2016, pp. 1–6. IEEE (2016)

  26. Teli, S.P., Biradar, S.K.: Effective email classification for spam and non-spam. Int. J. Adv. Res. Comput. Softw. Eng. 4, 2014 (2014)

    Google Scholar 

  27. Basnet, R., Mukkamala, S., Sung, A.H.: Detection of phishing attacks: a machine learning approach. In: Proceedings of the Soft computing applications in industry, pp. 373–383. Springer (2008)

  28. Moradpoor, N., Clavie, B., Buchanan, B.: Employing machine learning techniques for detection and classification of phishing emails. Comput. Conf. 2017, 149–156 (2017)

    Google Scholar 

  29. Smadi, S., Aslam, N., Zhang, L., Alasem, R., Hossain, M.A.: Detection of phishing emails using data mining algorithms. In: Proceedings of the 2015 9th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), pp. 1–8. IEEE (2015)

  30. Sheng, S., Holbrook, M., Kumaraguru, P., Cranor, L.F., Downs, J.: Who falls for phish? a demographic analysis of phishing susceptibility and effectiveness of interventions. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp. 373–382 (2010)

  31. Jagatic, T.N., Johnson, N.A., Jakobsson, M., Menczer, F.: Social phishing. Commun. ACM 50(10), 94–100 (2007)

    Article  Google Scholar 

  32. Kumaraguru, P., Sheng, S., Acquisti, A., Cranor, L.F., Hong, J.: Teaching johnny not to fall for phish. ACM Trans. Internet Technol. 10(2), 1–31 (2010)

    Article  Google Scholar 

  33. Kumaraguru, P., Cranshaw, J., Acquisti, A., Cranor,L., Hong, J., Blair, M.A., Pham, T.: School of phish: a real-world evaluation of anti-phishing training. In: Proceedings of the 5th Symposium on Usable Privacy and Security, pp. 1–12 (2009)

  34. Kumaraguru, P., Rhee, Y., Sheng, S., Hasan, S., Acquisti, A., Cranor, L.F., Hong, J.: Getting users to pay attention to anti-phishing education: evaluation of retention and transfer. In Proceedings of the Anti-phishing Working Groups 2nd Annual eCrime Researchers Summit, pp. 70–81 (2007)

  35. Parrish, J.L., Jr., Bailey, J.L., Courtney, J.F.: A Personality Based Model for Determining Susceptibility to Phishing Attacks, pp. 285–296. University of Arkansas, Little Rock (2009)

    Google Scholar 

  36. Kabali, H.K., Irigoyen, M.M., Nunez-Davis, R., Budacki, J.G., Mohanty, S.H., Leister, K.P., Bonner, R.L.: Exposure and use of mobile media devices by young children. Pediatrics 136(6), 1044–1050 (2015)

    Article  Google Scholar 

  37. Nikken, P., Schols, M.: How and why parents guide the media use of young children. J. Child Fam. Stud. 24(11), 3423–3435 (2015)

    Article  Google Scholar 

  38. Nicholson, J., Javed, Y., Dixon, M., Coventry, L., Ajayi, O.D., Anderson, P.: Investigating teenagers ability to detect phishing messages. In: Proceedings of the IEEE European Symposium on Security and Privacy Workshops (EuroS &PW). IEEE 2020, pp. 140–149 (2020)

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Funding

This work was supported by the Hashemite University and AL Zaytoonah University of Jordan.

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Authors and Affiliations

  1. Department of Information Technology, Faculty of prince Al-Hussien bin Abdullah for IT, The Hashemite University, P.O. Box 330127, 13133, Zarqa, Jordan

    Ala Mughaid, Salah Taamneh, Asma Alnajjar & Esraa Abu Elsoud

  2. Faculty of Science and IT, Al-Zaytoonah University of Jordan, Amman, Jordan

    Shadi AlZu’bi & Adnan Hnaif

Authors
  1. Ala Mughaid
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  2. Shadi AlZu’bi
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  3. Adnan Hnaif
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  4. Salah Taamneh
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  5. Asma Alnajjar
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  6. Esraa Abu Elsoud
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All Three Authors worked in an equivalent load at all stages to produce this research.

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Correspondence to Ala Mughaid.

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Mughaid, A., AlZu’bi, S., Hnaif, A. et al. An intelligent cyber security phishing detection system using deep learning techniques. Cluster Comput 25, 3819–3828 (2022). https://doi.org/10.1007/s10586-022-03604-4

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