Effective Spam Detection with Machine Learning

1Ahmed, N., Amin, R., Aldabbas, H., Koundal, D., Alouffi, B., & Shah, T. (2022). Machine Learning Techniques for Spam Detection in Email and IoT Platforms: Analysis and Research Challenges. Security and Communication Networks, 1862888. https://doi.org/10.1155/2022/1862888 Search in Google Scholar

2Alghoul, A., Ajrami, S. A., Jarousha, G. A., & Abu-Naser, S. S. (2018, November 30). Email Classification Using Artificial Neural Network. International Journal for Academic Development, 2(11), 8–14.Search in Google Scholar

3Awad, W. A., & ELseuofi, S. M. (2011). Machine learning methods for spam e-mail classification. International Journal of Computer Science and Information Technologies, 3(1), 173–184. Search in Google Scholar

4Bagić Babac, M. (2023). Emotion analysis of user reactions to online news. Information Discovery and Delivery, 51(2), 179–193. https://doi.org/10.1108/IDD-04-2022-0027 Search in Google Scholar

5Bassiouni, M., Ali, M., & El-Dahshan, E. A. (2018). Ham and spam e-mails classification using machine learning techniques. Journal of Applied Security Research, 13(3), 315–331. https://doi.org/10.1080/19361610.2018.1463136 Search in Google Scholar

6Bhuiyan, H., Ashiquzzaman, A., Juthi, T. I., Biswas, S., & Ara, J. (2018). A survey of existing e-mail spam filtering methods considering machine learning techniques. Global Journal of Computer Science and Technology, 18(2), 20–29. Search in Google Scholar

7Blanzieri, E., & Bryl, A. (2008). A survey of learning-based techniques of email spam filtering, Artificial Intelligence Review, 29(1), 63–92. https://doi.org/10.1007/s10462-009-9109-6Search in Google Scholar

8Blei, D., Ng, A., & Jordan, M. (2001). Latent Dirichlet Allocation. The Journal of Machine Learning Research, 3, 601–608. https://doi.org/10.5555/944919.944937Search in Google Scholar

9Brzić, B., Botički, I., & Bagić Babac, M. (2023). Detecting Deception Using Natural Language Processing and Machine Learning in Datasets on COVID-19 and Climate Change. Algorithms, 16, 221. https://doi.org/10.3390/a16050221Search in Google Scholar

10Cranor, L. F., & LaMacchia, B. A. (1998). Spam!. Communications of the ACM, 41(8), 74–83. https://doi.org/10.1145/280324.280336Search in Google Scholar

11Cvitanović, I., & Bagić Babac, M. (2022). Deep Learning with Self-Attention Mechanism for Fake News Detection. In M. Lahby, A.S.K. Pathan, Y. Maleh, & W.M.S. Yafooz (Eds.), Combating Fake News with Computational Intelligence Techniques (pp. 205–229). Springer, Switzerland.Search in Google Scholar

12Čemeljić, H., & Bagić Babac, M. (2023). Preventing Security Incidents on Social Networks: An Analysis of Harmful Content Dissemination Through Applications. Police and Security, 32(3), 239 – 270. https://doi.org/10.59245/ps.32.3.1 Search in Google Scholar

13Dada, E. G., Bassi, J. S., Chiroma, H., Adetunmbi, A. O., & Ajibuwa, O. E. (2019). Machine learning for email spam filtering: review, approaches and open research problems. Heliyon, 5(6), e01802. https://doi.org/10.1016/j.heliyon.2019.e01802 Search in Google Scholar

14Garg, P., & Girdhar, N. (2021). A Systematic Review on Spam Filtering Techniques based on Natural Language Processing Framework. 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India https://doi.org/10.1109/confluence51648.2021.9377042 Search in Google Scholar

15Garg, K. D., Shekhar, S., Kumar, A., Goyal, V., Sharma, B., Chengoden, R., & Srivastava, G. (2022). Framework for Handling Rare Word Problems in Neural Machine Translation System Using Multi-Word Expressions. Applied Sciences, 12(21), 11038. https://doi.org/10.3390/app122111038 Search in Google Scholar

16Goldberg, Y. (2014). word2vec Explained: deriving Mikolov et al.’s negative-sampling word-embedding method. arXiv:1402.3722 [cs.CL]. https://doi.org/10.48550/arXiv.1402.3722 Search in Google Scholar

17Hijawi, W., Faris, H., Alqatawna, J., Al-Zoubi, A. M., & Aljarah, I. (2017). Improving email spam detection using content based feature engineering approach. 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), Aqaba, Jordan, 2017, 1–6 https://doi.org/10.1109/aeect.2017.8257764 Search in Google Scholar

18Kaddoura, S., Chandrasekaran, G., Popescu, D. E., & Duraisamy, J. H. (2022). A systematic literature review on spam content detection and classification. PeerJ Computer Science, 8, e830. https://doi.org/10.7717/peerj-cs.830 Search in Google Scholar

19Kaggle. (2023). Email Spam Classification Dataset. Available at: https://www.kaggle.com/datasets/neildavid/email-spam-classification-from-shantanudhakad/code Search in Google Scholar

20Konagala, V., & Bano, S. (2020). Fake News Detection Using Deep Learning: Supervised Fake News Detection Analysis in Social Media With Semantic Similarity Method. In Thomas, J. J., Karagoz, P., Ahamed, B. B., & Vasant, P. (Eds.). (2020). Deep learning techniques and optimization strategies in big data analytics. IGI Global. 166–177. https://doi.org/10.4018/978-1-7998-1192-3.ch011 Search in Google Scholar

21Kontsewaya, Y., Antonov, E., & Artamonov, A. (2021). Evaluating the effectiveness of machine learning methods for spam detection. Procedia Computer Science, 190, 479–486. https://doi.org/10.1016/j.procs.2021.06.056 Search in Google Scholar

22Kudupudi, N. I. K. H. I. L., & Nair, S. (2021). Spam message detection using logistic regression. International Journal of Advanced Computer Science and Applications, 9(9), 815–818. Search in Google Scholar

23Kumar, N., Sonowal, S., & Nishant. (2020). Email spam detection using machine learning algorithms. Proceedings of the 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 108–113. https://doi.org/10.1109/ICIRCA48905.2020.9183098 Search in Google Scholar

24LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539 Search in Google Scholar

25Li, J., Cardie, C., & Li, S. (2013). Topic spam: a topic-model based approach for spam detection. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, 2, 217–221. Search in Google Scholar

26Marijić, A., & Bagić Babac, M. (2023). Predicting song genre with deep learning. Global Knowledge, Memory and Communication. Ahead-of-print. https://doi.org/10.1108/GKMC-08-2022-0187 Search in Google Scholar

27Méndez, J. R., Cotos-Yañez, T. R., & Ruano-Ordas, D. (2019). A new semantic-based feature selection method for spam filtering. Applied Soft Computing, 76, 89–104. https://doi.org/10.1016/j.asoc.2018.12.008 Search in Google Scholar

28Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space, arXiv:1301.3781.[cs.CL]. https://doi.org/10.48550/arXiv.1301.3781 Search in Google Scholar

29Mohammed, S., Mohammed, O., Fiaidhi, J., Fong, S., & Kim, T. H. (2013). Classifying unsolicited bulk email (UBE) using python machine learning techniques. International Journal of Hybrid Information Technology, 6(1), 43–56. Search in Google Scholar

30Možnik, D., Delija, D., Tulčić, D., & Galinec, D. (2023). Cybersecurity and Cyber Defense Insights: The Complementary Conceptual model of Cyber resilience. ENTRENOVA-ENTerprise REsearch InNOVAtion, 9(1), 1–12. https://doi.org/10.54820/entrenova-2023-0001 Search in Google Scholar

31Nandhini, S., & Marseline. K. S, J. (2020). Performance Evaluation of Machine Learning Algorithms for Email Spam Detection. 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), 1–4. https://doi.org/10.1109/ic-ETITE47903.2020.312 Search in Google Scholar

32Olatunji, S. O. (2017). Extreme Learning machines and Support Vector Machines models for email spam detection. Proceedings of the 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), IEEE, Windsor, Canada, April 2017. https://doi.org/10.1109/CCECE.2017.7946806 Search in Google Scholar

33Orred, K. (2023). 2023 Spam Text Statistics: Are Spam Texts on the Rise? Available at: https://www.text-em-all.com/blog/spam-text-statistics Search in Google Scholar

34Parveen, P., & Halse, P. G. (2016). Spam Mail Detection using Classification. International Journal of Advanced Research in Computer and Communication Engineering, 5(6), 347–349. Search in Google Scholar

35Powers, D. M. (2020). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv:2010.16061 [cs.LG] https://doi.org/10.48550/arXiv.2010.16061 Search in Google Scholar

36Prieto, A., Prieto, B., Ortigosa, E. M., Ros, E., Pelayo, F., Ortega, J., & Rojas, I. (2016). Neural networks: An overview of early research, current frameworks and new challenges. Neurocomputing, 214, 242–268. https://doi.org/10.1016/j.neucom.2016.06.014 Search in Google Scholar

37Provost, J. (1999). Naive-Bayes vs. Rule-Learning in Classification of Email. Available at: https://www.cs.utexas.edu/ftp/AI-Lab/tech-reports/UT-AI-TR-99-284.pdf Search in Google Scholar

38Puh, K., & Bagić Babac, M. (2023a). Predicting sentiment and rating of tourist reviews using machine learning. Journal of Hospitality and Tourism Insights, 6(3), 1188–1204. https://doi.org/10.1108/JHTI-02-2022-0078 Search in Google Scholar

39Puh, K., & Bagić Babac, M. (2023b). Predicting stock market using natural language processing. American Journal of Business, 38(2), 41–61. https://doi.org/10.1108/AJB-08-2022-0124 Search in Google Scholar

40Rahmad, F., Suryanto, Y., & Ramli, K. (2020). Performance comparison of anti-spam technology using confusion matrix classification. In IOP Conference Series: Materials Science and Engineering, 879(1), 012076. https://doi.org/10.1088/1757-899X/879/1/012076 Search in Google Scholar

41Sadia, A., Bashir, F., Khan, R. Q., Bashir, A., & Khalid, A. (2023). Comparison of Machine Learning Algorithms for Spam Detection. Journal of Advances in Information Technology, 14(2), 178–184. https://doi.org/10.12720/jait.14.2.178-184 Search in Google Scholar

42Sahoo, S. R., & Gupta, B. B. (2021). Multiple features based approach for automatic fake news detection on social networks using deep learning. Applied Soft Computing, 100, 106983. https://doi.org/10.1016/j.asoc.2020.106983 Search in Google Scholar

43Shahariar, G. M., Biswas, S., Omar, F., Shah, F. M. & Hassan, S. B., (2019). Spam Review Detection Using Deep Learning. 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). Vancouver, BC, Canada. 27–33. https://doi.org/10.1109/IEMCON.2019.8936148 Search in Google Scholar

44Sheneamer, A. (2021). Comparison of Deep and Traditional Learning Methods for Email Spam Filtering. International Journal of Advanced Computer Science and Applications (IJACSA), 12(1). https://doi.org/10.14569/IJACSA.2021.0120164 Search in Google Scholar

45Siddique, Z. B., Khan, M. A., Din, I. U., Almogren, A., Mohiuddin, I., & Nazir, S. (2021). Machine Learning-Based Detection of Spam Emails. Scientific Programming, 2021, 6508784. https://doi.org/10.1155/2021/6508784 Search in Google Scholar

46Sinha, A., & Singh, S. (2020). A Detailed study on email spam filtering techniques. International Journal of Data Science and Analytics, 10(3), 1–34. Search in Google Scholar

47Tembhurne, J. V., Almin, M. M., & Diwan, T. (2022). Mc-DNN: Fake News Detection Using Multi-Channel Deep Neural Networks. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1–20. https://doi.org/10.4018/ijswis.295553 Search in Google Scholar

48uSMS-GH.com. (2022). Spam text. Available: https://usmsgh.com/spam-text/ Search in Google Scholar

49Vrigazova, B. (2021). The proportion for splitting data into training and test set for the bootstrap in classification problems. Business Systems Research: International Journal of the Society for Advancing Innovation and Research in Economy, 12(1), 228–242. https://doi.org/10.2478/bsrj-2021-0015 Search in Google Scholar

50Vyas, T., Prajapati, P., & Gadhwal, s. (2015). A survey and evaluation of supervised machine learning techniques for spam e-mail filtering. 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, 1–7, http://doi.org/10.1109/ICECCT.2015.7226077 Search in Google Scholar

51Yan, J., & Lee, J. (2005). Degradation Assessment and Fault Modes Classification Using Logistic Regression, ASME. Journal of Manufacturing Science and Engineering, 127(4), 912–914. https://doi.org/10.1115/1.1962019 Search in Google Scholar