International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
권호 표지
DOI QR코드

DOI QR Code

Phishing Email Detection Using Machine Learning Techniques

  • Alammar, Meaad (College of Science, Department of Computer Science and Information in Majmaah University) ;
  • Badawi, Maria Altaib (College of Science, Department of Computer Science and Information in Majmaah University)
  • 투고 : 2022.05.05
  • 발행 : 2022.05.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Email phishing has become very prevalent especially now that most of our dealings have become technical. The victim receives a message that looks as if it was sent from a known party and the attack is carried out through a fake cookie that includes a phishing program or through links connected to fake websites, in both cases the goal is to install malicious software on the user's device or direct him to a fake website. Today it is difficult to deploy robust cybersecurity solutions without relying heavily on machine learning algorithms. This research seeks to detect phishing emails using high-accuracy machine learning techniques. using the WEKA tool with data preprocessing we create a proposed methodology to detect emails phishing. outperformed random forest algorithm on Naïve Bayes algorithms by accuracy of 99.03 %.

키워드

과제정보

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.

참고문헌

  1. Soni, P., Pawar, M., & Goyal, S. (2019). A Survey on Detection and Defense from Phishing.
  2. 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
  3. "Internet Crime Report 2020" (PDF). FBI Internet Crime Complaint Centre. U.S. Federal Bureau of Investigation. Retrieved February 10, 2022.
  4. 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.
  5. 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.
  6. 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
  7. 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
  8. 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
  9. 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
  10. Machine Learning Random Forest Algorithm - Javatpoint. (2021). Www.Javatpoint.Com. Retrieved February 17, 2022, from https://www.javatpoint.com/machine-learning-randomforest-algorithm
  11. Brownlee, J. (2019). Machine learning mastery with Weka. Ebook. Edition, 1(4).
  12. 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).
  13. Tan, Choon Lin (2018), "Phishing Dataset for Machine Learning: Feature Evaluation", Mendeley Data, V1, doi: 10.17632/h3cgnj8hft.1
  14. 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
  15. 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.
  16. Toolan, F., & Carthy, J. (2010, October). Feature selection for spam and phishing detection. In 2010 eCrime Researchers Summit (pp. 1-12). IEEE.