TY - GEN
T1 - A Phishing Detection System based on Machine Learning
AU - Wu, Che Yu
AU - Kuo, Cheng Chung
AU - Yang, Chu Sing
PY - 2019/8
Y1 - 2019/8
N2 - As the Internet has become an essential part of human beings' lives, a growing number of people are enjoying the convenience brought by the Internet, while more are attacks coming from on the dark side of the Internet. Based on some weaknesses of human nature, hackers have designed confusing phishing pages to entice web viewers to proactively expose their privacy, sensitive information.In this article, we propose a URL-based detection system - combining the URL of the web page URL and the URL of the web page source code as features, import Levenshtein Distance as the algorithm for calculating the similarity of strings, and supplemented by the machine learning architecture. Due to the great accuracy in small sample numbers and binary classification, we implement Support-vector machine to be the machine learning algorithm model in our system. The system is designed to provide high accuracy and low false positive rate detection results for unknown phishing pages.
AB - As the Internet has become an essential part of human beings' lives, a growing number of people are enjoying the convenience brought by the Internet, while more are attacks coming from on the dark side of the Internet. Based on some weaknesses of human nature, hackers have designed confusing phishing pages to entice web viewers to proactively expose their privacy, sensitive information.In this article, we propose a URL-based detection system - combining the URL of the web page URL and the URL of the web page source code as features, import Levenshtein Distance as the algorithm for calculating the similarity of strings, and supplemented by the machine learning architecture. Due to the great accuracy in small sample numbers and binary classification, we implement Support-vector machine to be the machine learning algorithm model in our system. The system is designed to provide high accuracy and low false positive rate detection results for unknown phishing pages.
UR - http://www.scopus.com/inward/record.url?scp=85074202780&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074202780&partnerID=8YFLogxK
U2 - 10.1109/ICEA.2019.8858325
DO - 10.1109/ICEA.2019.8858325
M3 - Conference contribution
T3 - Proceedings - 2019 International Conference on Intelligent Computing and Its Emerging Applications, ICEA 2019
SP - 28
EP - 32
BT - Proceedings - 2019 International Conference on Intelligent Computing and Its Emerging Applications, ICEA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Intelligent Computing and Its Emerging Applications, ICEA 2019
Y2 - 30 August 2019 through 1 September 2019
ER -