Open AccessResearch on Phishing Websitedetection Based on R-SVM algorithm
Author(s) -
Shuting Hu,
Jiawei Li,
Mingmeng Liu,
Bin Pan
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012058
Subject(s) - phishing , computer science , support vector machine , the internet , realization (probability) , data mining , big data , information sensitivity , computer security , machine learning , world wide web , mathematics , statistics
The Internet has brought us convenience, at the same timeimportant personal information has been exposed. Once the information is leaked, it may cause huge economic losses to individuals. Therefore, it is urgent to realize network security. The traditional method of detecting phishing websites is aimed at big data. Due to the relatively large amount of data, it indirectly causes low efficiency and often fails to solve user needs in a short time. In the detection, the operation is actually performed on the small data stream. If the small data is fundamentally classified, the efficiency will be greatly improved. In summary, based on the realization that we can identify phishing websites, we mainly solve the problems of speed and accuracy. Therefore, this paper uses the R-SVM algorithm to study the data to reduce the time spent on detecting samples and improve efficiency.