PREDIKSI EMAIL PHISING MENGGUNAKAN SUPPORT VECTOR MACHINE

Chaerul Umam, L. Budi Handoko

Abstract


Email phising merupakan salah satu bentuk kejahatan di internet yang dapat merugikan banyak orang. Ketika seseorang sudah terkena phising maka data data orang tersebut dapat hilang dan digunakan oleh orang yang tidak bertanggung jawab. Pada penelitian ini, akan melakukan proses klasiifkasi email phising dengan menggunakan bantuan machine learning yaitu algoritma SVM. Dataset yang digunakan pada penelitian ini yaiitu merupakan dataset yang berisi body email yang terdiri dari total 18650 data yang terdiri dari 11322 data safe email dan 7328 data phising email. Dari data tersebut, akan dibagi menjadi 70% data pelatihan dan 30% data pengujian. Setelah dilakukan proses pengujian pada penelitian ini, algoritma SVM yang digunakan mendapatkan akurasi pengujian sebesar 84.56%.


Full Text:

PDF

References


Salloum, S., Gaber, T., Vadera, S., & Shaalan, K. (2022). A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques. In IEEE Access (Vol. 10, pp. 65703–65727). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2022.3183083

Alhogail, A., & Alsabih, A. (2021). Applying machine learning and natural language processing to detect phishing email. Computers and Security, 110. https://doi.org/10.1016/j.cose.2021.102414

Bi, Q., Goodman, K. E., Kaminsky, J., & Lessler, J. (2019). What is machine learning? A primer for the epidemiologist. American Journal of Epidemiology, 188(12), 2222–2239. https://doi.org/10.1093/aje/kwz189

Sah, S. (2020). Machine Learning: A Review of Learning Types. https://doi.org/10.20944/preprints202007.0230.v1

Boateng, E. Y., Otoo, J., & Abaye, D. A. (2020). Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review. Journal of Data Analysis and Information Processing, 08(04), 341–357. https://doi.org/10.4236/jdaip.2020.84020

Muthukrishnan, S., Krishnaswamy, H., Thanikodi, S., Sundaresan, D., & Venkatraman, V. (2020). Support vector machine for modelling and simulation of heat exchangers. Thermal Science, 24(1PartB), 499–503. https://doi.org/10.2298/TSCI190419398M

V. Kumar and B. Subba, "A TfidfVectorizer and SVM based sentiment analysis framework for text data corpus," 2020 National Conference on Communications (NCC), Kharagpur, India, 2020, pp. 1-6, doi: 10.1109/NCC48643.2020.9056085.

Akuma, S., Lubem, T., & Adom, I. T. (2022). Comparing Bag of Words and TF-IDF with different models for hate speech detection from live tweets. International Journal of Information Technology (Singapore), 14(7), 3629–3635. https://doi.org/10.1007/s41870-022-01096-4

Krstinić, D., Braović, M., Šerić, L., & Božić-Štulić, D. (2020). Multi-label Classifier Performance Evaluation with Confusion Matrix. 01–14. https://doi.org/10.5121/csit.2020.100801

Khairandish, M. O., Sharma, M., Jain, V., Chatterjee, J. M., & Jhanjhi, N. Z. (2022). A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images. IRBM, 43(4), 290–299. https://doi.org/10.1016/j.irbm.2021.06.003

Styawati, S., & Mustofa, K. (2019). A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 13(3), 219. https://doi.org/10.22146/ijccs.41302

Vijayarajeswari, R., Parthasarathy, P., Vivekanandan, S., & Basha, A. A. (2019). Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform. Measurement: Journal of the International Measurement Confederation, 146, 800–805. https://doi.org/10.1016/j.measurement.2019.05.083

Ma, T. M., Yamamori, K., & Thida, A. (2020). A Comparative Approach to Naïve Bayes Classifier and Support Vector Machine for Email Spam Classification. 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020, 324–326. https://doi.org/10.1109/GCCE50665.2020.9291921




DOI: https://doi.org/10.30998/semnasristek.v8i01.7138

Refbacks

  • There are currently no refbacks.


Prosiding SEMNAS RISTEK indexed by: