Acknowledgement
First and foremost praise is to Allah. his constant grace and mercy were with us during life and throughout this project duration. we are extremely grateful to our parents for their love and continued support in preparing for my future. we would like to thank the Department of Computer Science & information, Majmaah University for their constant support, guidance, and encouragement. We appreciate the discussions, suggestions, criticism, and support of our colleagues, and friends, We would also like to thank them for all the aspects that facilitated the smooth work of my project. Finally, we owe everything to our family who at every point of our personal and academic life, supported and motivated us, and longed to see this accomplishment come true.
References
- Soni, P., Pawar, M., & Goyal, S. (2019). A Survey on Detection and Defense from Phishing.
- Ferreira, A., & Teles, S. (2019). Persuasion: How phishing emails can influence users and bypass security measures. International Journal of Human-Computer Studies, 125, 19-31. https://doi.org/10.1016/j.ijhcs.2018.12.004
- "Internet Crime Report 2020" (PDF). FBI Internet Crime Complaint Centre. U.S. Federal Bureau of Investigation. Retrieved February 10, 2022.
- Lin, T., Capecci, D. E., Ellis, D. M., Rocha, H. A., Dommaraju, S., Oliveira, D. S., & Ebner, N. C. (2019). Susceptibility to spear-phishing emails: Effects of internet user demographics and email content. ACM Transactions on Computer-Human Interaction (TOCHI), 26(5), 1-28.
- Mishra, P., Varadharajan, V., Tupakula, U., & Pilli, E. S. (2018). A detailed investigation and analysis of using machine learning techniques for intrusion detection. IEEE Communications Surveys & Tutorials, 21(1), 686-728.
- Tsymbal, O. (2022, January 20). 5 Essential Machine Learning Algorithms For Business Applications. MobiDev. Retrieved February 9, 2022, from https://mobidev.biz/blog/5-essential-machine-learning-techniques
- Concept of Machine Learning - Python Numerical Methods. (2021). Book. Retrieved February10,2022,from https://pythonnumericalmethods.berkeley.edu/notebooks/chapter25.01-Concept-of Machine-Learning.html
- Boonprong, S., Cao, C., Chen, W., Ni, X., Xu, M., & Acharya, B.K. (2018). The Classification of Noise-Afflicted Remotely Sensed Data Using Three Machine-Learning Techniques: Effect of Different Levels and Types of Noise on Accuracy. ISPRS Int. J. Geo Inf., 7, 274. https://doi.org/10.3390/ijgi7070274
- Grigorescu, A., Maer-Matei, M. M., Mocanu, C., & Zamfir, A. M. (2020). Key drivers and skills needed for innovative companies focused on sustainability. Sustainability, 12(1), 102. https://doi.org/10.3390/su12010102
- Machine Learning Random Forest Algorithm - Javatpoint. (2021). Www.Javatpoint.Com. Retrieved February 17, 2022, from https://www.javatpoint.com/machine-learning-randomforest-algorithm
- Brownlee, J. (2019). Machine learning mastery with Weka. Ebook. Edition, 1(4).
- Fong, S., Biuk-Aghai, R. P., & Millham, R. C. (2018, February). Swarm search methods in weka for data mining. In Proceedings of the 2018 10th international conference on machine learning and computing (pp. 122-127).
- Tan, Choon Lin (2018), "Phishing Dataset for Machine Learning: Feature Evaluation", Mendeley Data, V1, doi: 10.17632/h3cgnj8hft.1
- K. (2021, Aug 3). GitHub - kregg34/EmailHeaderAnomalyDetection: Using machine learning and features extracted from email headers to detect anomalies (i.e., spam, phishing) in email datasets. [Dataset]. https://github.com/kregg34/EmailHeaderAnomalyDetection
- Pandey, A. K., & Rajpoot, D. S. (2016, December). A comparative study of classification techniques by utilizing WEKA. In 2016 International Conference on Signal Processing and Communication (ICSC) (pp. 219-224). IEEE.
- Toolan, F., & Carthy, J. (2010, October). Feature selection for spam and phishing detection. In 2010 eCrime Researchers Summit (pp. 1-12). IEEE.