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Original Article
Novel Deep Learning Approach for Securing Email Communications against Emerging Cyber Threats
S. Senthamarai Selvi1
T. Arthika2
1Assistant professor, HOD, Department of MCA, Vivekananda Institute of Information and Management Studies,Tiruchengode, Namakkal, Tamil Nadu, India. 2Department of MCA, Vivekananda Institute of Information and Management Studies, Tiruchengode, Namakkal,Tamil Nadu, India
Published Online: May-June 2025
Pages: 76-80
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20250503011
References
1. W. Park, N.M.F. Qureshi and D.R. Shin, "Pseudo nlp joint spam classification technique for big data cluster", Computers Materials
& Continua, vol. 71, no. 1, pp. 517-535, 2022.
2. A.Karim, S. Azam, B. Shanmugam, K. Kannoorpatti, and M. Alazab,``A comprehensive survey for intelligent spam email
detection,''IEEE Access, vol. 7, pp. 168261-168295, 2019.
3. A.S. Aski and N. K. Sourati, ``Proposed efficient algorithm to filter spam using machine learning techniques,'' Pacific Sci. Rev. A,
Natural Sci. Eng., vol. 18, no. 2, pp. 145-149, Jul. 2016.
4. E. Bauer. 15 Outrageous Email Spam Statistics That Still Ring True in 2018, RSS. Accessed: Oct. 10, 2020. [Online]. Available:
https://www.propellercrm.com/blog/email-spam-statistics.
5. Mujtaba, L. Shuib, R. G. Raj, N. Majeed, and M. A. Al-Garadi, ``Email classification research trends: Review and open issues,'' IEEE
Access, vol. 5, pp. 9044-9064, 2017.
6. I. Idris, A. Selamat, N. T. Nguyen, S. Omatu, O. Krejcar, K. Kuca, and M. Penhaker, ``A combined negative selection algorithm-particle
swarm optimization for an email spam detection system,'' Eng. Appl. Artif. Intell., vol. 39, pp. 33-44, Mar. 2015.
7. K. Agarwal and T. Kumar, ``Email spam detection using integrated approach of naïve Bayes and particle swarm optimization,'' in
Proc. 2nd Int. Conf. Intell. Comput. Control Syst. (ICICCS), Jun. 2018, pp. 685-690.
8. K. Singh, S. Bhushan, and S. Vij, ``Filtering spam messages and mails using fuzzy C means algorithm,'' in Proc. 4th Int. Conf. Internet
Things, Smart Innov. Usages (IoT-SIU), Apr. 2019, pp. 1-5.
9. M. Shuaib, O. Osho, I. Ismaila, and J. K. Alhassan, ``Comparative analysis of classification algorithms for email spam detection,'' Int.
J. Comput. Netw. Inf. Secur., vol. 10, no. 1, pp. 60-67, Aug. 2018.
10. N. Moradpoor, B. Clavie, and B. Buchanan, ``Employing machine learning techniques for detection and classification of phishing
emails,'' in Proc. Comput. Conf., Jul. 2017, pp. 149-156.
11. Q. Li, M. Cheng, J. Wang, and B. Sun, ``LSTM based phishing detection for big email data,'' IEEE Trans. Big Data, early access, Mar.
12, 2020, doi: v10.1109/TBDATA.2020.2978915.
12. R. S. H. Ali and N. E. Gayar, ``Sentiment analysis using unlabeled email data,'' in Proc. Int. Conf. Comput. Intell. Knowl. Economy
(ICCIKE), Dec. 2019, pp. 328-333.
13. S. Sinha, I. Ghosh, and S. C. Satapathy, ``A study for ANN model for spam classification,'' in Intelligent Data Engineering and
Analytics. Singapore: Springer, 2021, pp. 331-343.
14. T. Gangavarapu, C. D. Jaidhar, and B. Chanduka, ``Applicability of machine learning in spam and phishing email filtering: Review
and approaches,'' Artif. Intell. Rev., vol. 53, no. 7, pp. 5019-5081, Oct. 2020, doi: 10.1007/s10462-020-09814-9.
15. Y. Kaya and Ö. F. Ertu§rul, ``A novel approach for spam email detection based on shifted binary patterns,'' Secur. Commun. Netw.,
vol. 9, no. 10, pp. 1216-1225, Jul. 2016.
& Continua, vol. 71, no. 1, pp. 517-535, 2022.
2. A.Karim, S. Azam, B. Shanmugam, K. Kannoorpatti, and M. Alazab,``A comprehensive survey for intelligent spam email
detection,''IEEE Access, vol. 7, pp. 168261-168295, 2019.
3. A.S. Aski and N. K. Sourati, ``Proposed efficient algorithm to filter spam using machine learning techniques,'' Pacific Sci. Rev. A,
Natural Sci. Eng., vol. 18, no. 2, pp. 145-149, Jul. 2016.
4. E. Bauer. 15 Outrageous Email Spam Statistics That Still Ring True in 2018, RSS. Accessed: Oct. 10, 2020. [Online]. Available:
https://www.propellercrm.com/blog/email-spam-statistics.
5. Mujtaba, L. Shuib, R. G. Raj, N. Majeed, and M. A. Al-Garadi, ``Email classification research trends: Review and open issues,'' IEEE
Access, vol. 5, pp. 9044-9064, 2017.
6. I. Idris, A. Selamat, N. T. Nguyen, S. Omatu, O. Krejcar, K. Kuca, and M. Penhaker, ``A combined negative selection algorithm-particle
swarm optimization for an email spam detection system,'' Eng. Appl. Artif. Intell., vol. 39, pp. 33-44, Mar. 2015.
7. K. Agarwal and T. Kumar, ``Email spam detection using integrated approach of naïve Bayes and particle swarm optimization,'' in
Proc. 2nd Int. Conf. Intell. Comput. Control Syst. (ICICCS), Jun. 2018, pp. 685-690.
8. K. Singh, S. Bhushan, and S. Vij, ``Filtering spam messages and mails using fuzzy C means algorithm,'' in Proc. 4th Int. Conf. Internet
Things, Smart Innov. Usages (IoT-SIU), Apr. 2019, pp. 1-5.
9. M. Shuaib, O. Osho, I. Ismaila, and J. K. Alhassan, ``Comparative analysis of classification algorithms for email spam detection,'' Int.
J. Comput. Netw. Inf. Secur., vol. 10, no. 1, pp. 60-67, Aug. 2018.
10. N. Moradpoor, B. Clavie, and B. Buchanan, ``Employing machine learning techniques for detection and classification of phishing
emails,'' in Proc. Comput. Conf., Jul. 2017, pp. 149-156.
11. Q. Li, M. Cheng, J. Wang, and B. Sun, ``LSTM based phishing detection for big email data,'' IEEE Trans. Big Data, early access, Mar.
12, 2020, doi: v10.1109/TBDATA.2020.2978915.
12. R. S. H. Ali and N. E. Gayar, ``Sentiment analysis using unlabeled email data,'' in Proc. Int. Conf. Comput. Intell. Knowl. Economy
(ICCIKE), Dec. 2019, pp. 328-333.
13. S. Sinha, I. Ghosh, and S. C. Satapathy, ``A study for ANN model for spam classification,'' in Intelligent Data Engineering and
Analytics. Singapore: Springer, 2021, pp. 331-343.
14. T. Gangavarapu, C. D. Jaidhar, and B. Chanduka, ``Applicability of machine learning in spam and phishing email filtering: Review
and approaches,'' Artif. Intell. Rev., vol. 53, no. 7, pp. 5019-5081, Oct. 2020, doi: 10.1007/s10462-020-09814-9.
15. Y. Kaya and Ö. F. Ertu§rul, ``A novel approach for spam email detection based on shifted binary patterns,'' Secur. Commun. Netw.,
vol. 9, no. 10, pp. 1216-1225, Jul. 2016.
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