Phishing website detection method based on multi-feature fusion

Abstract

The invention discloses a phishing website detection method based on multi-feature fusion. The phishing website on the internet can be detected in real time by the method. The URL features, the HTML features and the text vectors based on TF-IDF are firstly extracted according to the URL of the webpage; then the text vectors are classified by using Logistic regression, Logistic regression featuresare constructed and multi-feature fusion of the Logistic regression features and the webpage URL features and the HTML features is performed; and finally the XGBoost (eXtreme gradient boosting) modelis trained and classified prediction is performed on the phishing website to be detected. Multiple key features are omnidirectionally extracted from multiple dimensions based on the URL of the website, the problem of high feature dimension of the text vectors can be effectively solved by using the Logistic regression feature fusion method and the operation efficiency can be greatly enhanced in comparison with that of the existing feature fusion method; besides, the XGBoost classification model can further enhance the phishing website detection accuracy in comparison with the conventional classification model so as to reduce the missing report rate of phishing website detection.

Description

technical field [0001] The invention relates to a method for detecting phishing websites based on multi-feature fusion. The method extracts the characteristics of phishing websites from multiple dimensions in an all-round way, uses machine learning methods to classify and improve the classification accuracy, and can detect phishing websites on the Internet in real time. It belongs to the network The field of space security technology. Background technique [0002] In recent years, with the rapid development of the Internet, the deficiencies in the security of the Internet architecture have become increasingly apparent, and various security issues such as phishing, cybercrime, and privacy leaks have become more and more prominent. There is no national security without cybersecurity, and cyberspace security has become a difficult problem that all countries in the world must face and solve together. Among various network security issues, phishing is a criminal act of stealing ...

AI Technical Summary

Problems solved by technology

This method mainly includes the process of feature extraction, feature fusion, and classification prediction. It can extract the features of phishing websites from multiple dimensions, effectively solve the problem of high dimensionality of text vector features, and extend the XGBoost classification model to the detection of phishing websites. , improve detection accuracy and reduce detection false negative rate

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