Sun’iy intelekt yordamida phishing havolalarni aniqlash
Kalit so'zlar:
Phishing havo, sun’iy intelekt, logistik regressiya, SVM, Support Vector MachineAnnotatsiya
Ushbu maqolada phishing havolalarni aniqlashda sun’iy intellekt asosidagi avtomatik yondashuv taklif etiladi. An’anaviy aniqlash usullari tez o‘zgarayotgan phishing usullariga qarshi yetarli samaradorlikka ega emas. Shu bois, maqolada mashina o‘rganish va chuqur o‘rganish algoritmlari yordamida havolalarning xususiyatlari tahlil qilinib, phishing havolalarni yuqori aniqlik bilan aniqlashga qaratilgan model ishlab chiqildi va sinovdan o‘tkazildi
Библиографические ссылки
1. Chiew, K. L., Tan, C. L., & Wong, K. W. (2018). A survey of phishing detection. Computers
& Security, 83, 80–92. https://doi.org/10.1016/j.cose.2018.02.013
2. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
https://doi.org/10.1007/BF00994018
3. Ma, J., Saul, L. K., Savage, S., & Voelker, G. M. (2009). Beyond blacklists: Learning to detect
malicious web sites from suspicious URLs. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1245–1254. https://doi.org/10.1145/1557019.1557123
4. Sahingoz, O. K., Buber, E., Demir, O., & Diri, B. (2019). Machine learning based phishing detection from URLs. Expert Systems with Applications, 117, 345–357. https://doi.org/10.1016/j.eswa.2018.10.050
5. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
https://doi.org/10.1007/BF00994018
6. Kleinbaum, D. G., & Klein, M. (2010). Logistic Regression: A Self-Learning Text (3rd ed.).
Springer.
7. Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient
(MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21(1), 6. https://doi.org/10.1186/s12864-019-6413-7
