| Title |
Research on phishing email detection based on URL parameters using machine learning algorithms / |
| Authors |
Tubyte, Milda ; Paulauskaite-Taraseviciene, Agne |
| Full Text |
|
| Is Part of |
CEUR workshop proceedings: IVUS 2021: Information society and university studies 2021: Proceedings of the 26th international conference on information society and university studies (IVUS 2021), Kaunas, Lithuania, April 23, 2021 / edited by: I. Veitaitė, A. Lopata, T. Krilavičius, M. Woźniak.. Aachen : CEUR-WS. 2021, vol. 2915, art. no. 3, p. 18-26.. ISSN 1613-0073 |
| Keywords [eng] |
phishing ; URL, machine learning ; cybersecurity ; classification |
| Abstract [eng] |
Abstract – phishing is the most frequent data breach problem in cybersecurity. Cyber scammers use the phishing approach to outwit or obtain sensitive information without a consumer agreement. The victims might receive an email that promotes clicking on the following malicious links that lead to sensitive data leaks. This problem is especially relevant to large companies. Attackers tend to prepare emails that contain work-related information and include familiar keywords in phishing URL. This paper addresses the URL Boolean classification problem using various machine learning methods such as Support Vector Machine, Random Forest, Decision Tree, Linear Discriminant Analysis, and Logistic Regression. This paper provides a comparative study on these algorithms applied for two different URL classification datasets. |
| Published |
Aachen : CEUR-WS |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2021 |
| CC license |
|
