Detection of Violent E-mails Using Fuzzy Logic
|© 2021 by IJCTT Journal|
|Year of Publication : 2021|
|Authors : Victoria Oluwatoyin Oyekunle, Prince Oghenekaro Asagba, Fubara Egbono|
|DOI : 10.14445/22312803/IJCTT-V69I3P114|
How to Cite?
Victoria Oluwatoyin Oyekunle, Prince Oghenekaro Asagba, Fubara Egbono, "Detection of Violent E-mails Using Fuzzy Logic," International Journal of Computer Trends and Technology, vol. 69, no. 3, pp. 79-84, 2021. Crossref, 10.14445/22312803/IJCTT-V69I3P114
People all around the world spend billions of e-mail messages daily, and the use of mobile e-mail (e-mail sent via a mobile device) is growing at an astounding rate. Despite its advantages, one of the biggest threats to an email today is Violent and phishing e-mail. This research improves the detection and filtering of violent and phishing e-mails by implementing a fuzzy Logic detection model that classifies e-mails into classes’ violent, phishing and ham and then determines how harmful the classified e-mails are. Incoming e-mails were classified based on how well their features as compared with their rank values satisfied the stated fuzzy rules. From the results, output e-mail classes and their corresponding degrees of threats were provided at high accuracy and improved speed from Moderate to High or Very High.
E-mail, Fuzzy logic, Ham, Phishing, Violent E-mail, Artificial Intelligence
 Violent e-mail scams are on the rise: https://www.techradar.com/news/violent-e-mail-scams-are-on-therise/ retrieved May 28, (2020).
 R. E. Sorace, How to handle E-mail harassment: https://www.huffpost.com/entry/how-to-handle-e-mail haras_b_5606031/ retrieved May 28, (2020).
 Avoid getting in trouble with e-mail: https://kb.iu.edu/d/afnf retrieved May 28, (2020).
 M. Sahami, S. Dumais, D. Heckerman, E. Horvitz, A Bayesian approach to filtering junk e-mail, In Proceedings AAAI Workshop on Learning for Text Categorization, (1998)
 Y. Hong, L. Qihe, Z. Shijie, L. Yang, A Spam Filtering Method Based on Multi-Modal Fusion. Applied Sciences,(9) (2019) 1152.
 A. Saleh, A. Karim, B. Shanmugam, S. Azam, K. Kannoorpatti, M. Jonkman, F. De Boer, An Intelligent Spam Detection Model Based on Artificial Immune System. In-formation (10) (2019) 209.
 M. .A. Shafi’I, M. S. A. Latiff, H. Chiroma, O. Osho, G. Abdul Salaam, A. I. Abubakar, T. Herawan., A review on mobile SMS spam filtering techniques. IEEE Access, (5) (2017) 15650-15666.
 E. G. Dada, J. S. Bassi, H. Chiroma, A. O. Adetunmbi, O.E. Ajibuwa., Machine learning for e-mail spam filtering: review, approaches and open research problems. Heliyon, 5(6) (2019) e01802.
 O. Abayomi-Alli, S. Misra, A. Abayomi-Alli, M. Odusami., A review of soft techniques for SMS spam classification: Methods, approaches, and applications. Engineering Applications of Artificial Intelligence, (86) (2019) 197-212.
 B. K. Dedeturk, B. Akay., Spam filtering using a logistic regression model trained by an artificial bee colony algorithm. Applied Soft Computing, PDCA12-70 data sheet,Opto Speed SA, Mezzovico, Switzerland, (10) (2020) 6229.
 J. R. Méndez, T. R. Cotos-Yañez, D. Ruano-Ordás, A new semantic based feature selection method for spam filtering. Applied Soft Computing, (76) (2019) 89-104.
 A. Fahfouh, J. Riffi, M. A. Mahraz, A. Yahyaouy, H. Tairi, PVDAE: A hybrid model for deceptive opinion spam based on neural network architectures. Expert Systems with Applications, 11(3) (2020) 517.
 N. Andrew, Jeff, Building High-level Features Using Large Scale Unsupervised Learning. Proceedings of the 29th International Conference on Machine Learning, Ed-inburgh, Scotland, UK, (2013) 1-13.
 R. Anju., V. Vaidhehi, E-mail Classification Using Machine Learning Algorithms. International Journal of Engineering and Technology (IJET), 9(2) (2017).
 I. Androutsopoulos., P. Georgios, K. Vangelis, S. Georgios, D.S. Constantine, S. Panagiotis., Learning to Filter Spam E-Mail: A Comparison of a Naïve Bayesian and a Memory-Based Approach, Proceedings of the workshop, Machine Learning and Textual Information Access, H. Zaragoza, P. Gallinari, and M. Rajman (Eds.), 4th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD-2000), Lyon, France, (2000) 1-13.
 B. Cetisli., Development of an adaptive neuro-fuzzy classifier using linguistic hedges: Part 1, Expert Systems with Applications, 37 (2010) 6093-6101.
 M. Dewan, D. Farid, Z. Li, M. R. Chowdhury, M.A. Hossain, Rebecca S., Hybrid Decision tree and Naïve Bayes classifiers for multi-class classification tasks. Expert Systems with Applications, 41 (2014) 1937–1946.
 R. Giyanani, M. Desai., Spam Detection using Natural Language Processing. IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661,p-ISSN: 2278-8727, 16(5) (2014) 116-119.
 I. Ismaila, S. Ali, N. Thanh Nguyen, S.O. Omatu, M. P. KamilKuca., A combined negative selection algorithm–particle swarm optimization for an e-mail Spam detection system. Engineering Applications of Artificial Intelligence 39 (2015) 33-44.
 A. Sharma, Anchal, SMS Spam Detection Using Neural Network Classifier. IEEE, (2014) 240-244.
 S. Seth, S. Biswas., Multimodal Spam Classification Using Deep learning Techniques. On Signal-Image Technology and Internet Based Systems, Proceedings of the International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Jai-pur, India, (2017).
 S. Mehta, U. Eranna, K. Soundararajan., A Fuzzy Technique for Classification of Intercepted Communication, International Journal of Communication Engineering Applications-IJCEA, 03(01) (2012).
 M. N. Marson, M. W El-Kharashi, Fayez Gebali., Targeting Spam control on middleboxes: Spam detection based on layer-3 E-mail content classification. Elsevier Com-puter Networks 53 (2009) 835– 848.
 P. Sudhakar., G. Poonkuzhali, K. Thiagarajan, R.K. Keshav, K. Sarukesi, Fuzzy Logic for E-Mail Spam Deduction, Proceedings of the International Conference on Applied Computer and Applied Computational Science, Vernice, Italy, (2011).
 G. Santhi, S. M. Wenisch, Sengutuvan, P., A Content-Based Classification of Spam Mails with Fuzzy Word Ranking, IJCSI International Journal of Computer Science Issues, 10 (3) (2013) 2. ISSN (Print): 1694-0814 | ISSN (Online): 1694-0784.
 H. Kotian., K. Gupta, J. Stephy., Using Fuzzy Logic for E-mail Spam Filtering. International Journal of Advanced Research in Computer Science and Software Engineering 5(10) (2015) 15-20.
 S. Almasan, W. Qaid, Khalid, A., I. Alqubat., Filtering Spam Using Fuzzy Expert System, Journal of Emerging Trends in Computing and Information Sciences 10(12) (2015) 655-660.
 Surendiran,R., and Alagarsamy,K., 2012. "An Extensive Survey on Mobile Security and Issues”. SSRG International Journal of Computer & organization Trends (IJCOT), 2(1), pp.39-46.