Yapay Zeka Teknikleri İle Gelen E-Postaların Ayrıştırılması

Yıl 2021, Sayı: 21, 690 - 696, 31.01.2021

Mete Yağanoğlu , Erdal Irmak

https://doi.org/10.31590/ejosat.841299

Öz

Teknolojik gelişmeler, bireyleri ve kuruluşları, iletişim kurmak ve bilgi paylaşmak için e-postalara daha bağımlı hale getirmektedir. E-postaların internet üzerinden önemli ve popüler bir iletişim olarak artan kullanımı, İnternet’i ve toplumu etkileyen ciddi bir tehdit oluşturmaktadır. Spam epostalar internet kullanıcıları için güvenlik sorunlarına sebep olmaktadır ve depolama, bant genişliği ve üretkenlik açısından kaynakları boşa harcamaktadır. İstenmeyen e-postaların hacmindeki artış, daha güvenilir ve sağlam antispam filtrelerin geliştirilmesi için yoğun bir ihtiyaç yaratmıştır. Bu nedenle, uyarlanabilir spam algılama modellerinin önerilmesi bir gereklilik haline gelmektedir. Bu çalışmada, spam e-postalarını başarılı bir şekilde tespit etmek ve filtrelemek için yapay zekaya dayalı akıllı bir algılama sistemi önerilmektedir Makine öğrenimi yöntemleri, mevcut verileri kullanarak en iyi modelleri oluşturmayı ve önceki veriler kullanılarak oluşturulan model yardımıyla yeni verileri en doğru şekilde analiz etmeyi amaçlamaktadır. Bu çalışmada, istenmeyen posta tespiti makine öğrenimi yöntemleri kullanılarak gerçekleştirilmiştir ve % 98,2 başarı oranına ulaşılmıştır.

Anahtar Kelimeler

Spam Tespiti, Doğal Dil İşleme, Yapay Zeka, Makine Öğrenmesi

Separation of Incoming E-Mails Through Artificial Intelligence Techniques

Öz

Technological developments are making individuals and organizations ever more dependent on e-mail to communicate and share information. Increasing use of e-mail as an important and popular method of communication poses potentially serious threats to the Internet and society. Spam e-mails cause security problems for internet users, and waste storage, bandwidth and productivity resources. The increase in the volume of spam e-mails has created an intense need for the development of more reliable and robust antispam filters. Therefore, it has become necessary to recommend adaptive spam detection models. In this paper, an intelligent system for the detection and filtering of spam e-mails is described. Machine learning methods aim to create the best models using the available data, and to analyze new data in the most accurate way, with the help of the model created using previous data. In this study, spam detection was carried out using machine learning methods. The classification achieved a success rate of 98,2% in spam detection.

Anahtar Kelimeler

Spam Detection, Natural Language Processing, Artificial Intelligence, Machine Learning

Kaynakça

• Al-Ajeli, A., Alubady, R., & Al-Shamery, E. S. “Improving spam email detection using hybrid feature selection and sequential minimal optimization”. Indonesian Journal of Electrical Engineering and Computer Science, 19(1), 535-542, 2020.
• AlMahmoud, A., Damiani, E., Otrok, H., & Al-Hammadi, Y. “Spamdoop: A privacy-preserving Big Data platform for collaborative spam detection”. IEEE Transactions on Big Data, 2017.
• Almeida, T. A., Hidalgo, J. M. G., & Yamakami, A. “Contributions to the study of SMS spam filtering: new collection and results”. In Proceedings of the 11th ACM symposium on Document engineering, pp. 259-262, 2011.
• Asghar, M. Z., Ullah, A., Ahmad, S., & Khan, A. “Opinion spam detection framework using hybrid classification scheme”. Soft computing, 24(5), 3475-3498, 2020.
• Christina, V., Karpagavalli, S., & Suganya, G. “Email spam filtering using supervised machine learning techniques”. International Journal on Computer Science and Engineering (IJCSE), 2(09), 3126-3129, 2010.
• Dada, E. G., Bassi, J. S., Chiroma, H., Adetunmbi, A. O., & Ajibuwa, O. E. “Machine learning for email spam filtering: review, approaches and open research problems”. Heliyon, 5(6), 2019.
• Deng, Z., Zhu, X., Cheng, D., Zong, M., & Zhang, S. “Efficient kNN classification algorithm for big data”. Neurocomputing, 195, 143-148, 2016.
• El-Alfy, E. S. M., & AlHasan, A. A. “Spam filtering framework for multimodal mobile communication based on dendritic cell algorithm”. Future Generation Computer Systems, 64, 98-107, 2016.
• Faris, H., Ala’M, A. Z., Heidari, A. A., Aljarah, I., Mafarja, M., Hassonah, M. A., & Fujita, H. “An intelligent system for spam detection and identification of the most relevant features based on evolutionary random weight networks”. Information Fusion, 48, 67-83, 2019.
• Gunawan, D., Rahmat, R. F., Putra, A., & Pasha, M. F. “Filtering Spam Text Messages by Using Twitter-LDA Algorithm”. IEEE International Conference on Communication, Networks and Satellite (Comnetsat), pp. 1-6, IEEE, 2018.
• Hidalgo, J. M. G., Almeida, T. A., & Yamakami, A. “On the validity of a new SMS spam collection”. 11th International Conference on Machine Learning and Applications, Vol. 2, pp. 240-245, IEEE, 2012.
• Katakis, I., Tsoumakas, G., & Vlahavas, I., E-mail mining: Emerging techniques for e-mail management. In Web Data Management Practices: Emerging Techniques and Technologies (pp. 220-243). IGI Global, 2007.
• Khamis, S. A., Foozy, C. F. M., Ab Aziz, M. F., & Rahim, N. “Header Based Email Spam Detection Framework Using Support Vector Machine (SVM) Technique”. In International Conference on Soft Computing and Data Mining, pp. 57-65,. Springer, Cham,2020.
• Kumar, V., Kumar, P., & Sharma, A. “Spam Email Detection using ID3 Algorithm and Hidden Markov Model”. In 2018 Conference on Information and Communication Technology (CICT) (pp. 1-6). IEEE, 2018.
• Liu, A. X., & Gouda, M. G. “Diverse firewall design. IEEE Transactions on Parallel and Distributed Systems”. 19(9), 1237-1251, 2008. Olatunji, S. O. “Improved email spam detection model based on support vector machines”. Neural Computing and Applications, 31(3), 691-699, 2019.
• Pelletier, L., Almhana, J., & Choulakian, V. “Adaptive filtering of spam”. In Proceedings. Second Annual Conference on Communication Networks and Services Research, pp. 218-224, IEEE, 2004.
• Sakkis, G., Androutsopoulos, I., Paliouras, G., Karkaletsis, V., Spyropoulos, C. D., & Stamatopoulos, P. “Stacking classifiers for anti-spam filtering of e-mail”. arXiv preprint cs/0106040., 2001.
• Saleh, A. J., Karim, A., Shanmugam, B., Azam, S., Kannoorpatti, K., Jonkman, M., & Boer, F. D. “An intelligent spam detection model based on artificial immune system”. Information, 10(6), 209, 2019.
• Shi, W., & Xie, M. “A reputation-based collaborative approach for spam filtering”. AASRI Procedia, 5, 220-227,2013.
• Sirivianos, M., Kim, K., & Yang, X. “Socialfilter: Introducing social trust to collaborative spam mitigation”. In 2011 Proceedings IEEE INFOCOM, pp. 2300-2308, IEEE,2011.
• Spirin, N., & Han, J. “Survey on web spam detection: principles and algorithms”. ACM SIGKDD explorations newsletter, 13(2), 50-64,2012.
• Tan, Y., Wang, Q., & Mi, G. “Ensemble decision for spam detection using term space partition approach”. IEEE transactions on cybernetics, 50(1), 297-309, 2018.
• Tekerek, A. “Support vector machine based spam SMS detection”. Politeknik Dergisi, 22(3), 779-784,2019.
• Torabi, Z. S., Nadimi-Shahraki, M. H., & Nabiollahi, A. “Efficient support vector machines for spam detection: a survey”. International Journal of Computer Science and Information Security, 13(1), 11,2015.
• Yao, J. “Automated Sentiment Analysis of Text Data with NLTK”. In Journal of Physics: Conference Series (Vol. 1187, No. 5, p. 052020). IOP Publishing, 2019.
• Zhu, Y., & Tan, Y. “Extracting discriminative information from e-mail for spam detection inspired by immune system”. In IEEE Congress on Evolutionary Computation (pp. 1-7). IEEE, 2010.