Fraudulent website identification and classification system based on machine learning

Feng Zhou; Xiaodong Liu

doi:10.1117/12.2681459

23 May 2023 Fraudulent website identification and classification system based on machine learning

Feng Zhou, Xiaodong Liu

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126452W (2023) https://doi.org/10.1117/12.2681459
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

With the rapid development of the Internet, there has been a high incidence of telecom fraud cases. Among the existing methods for identifying and classifying fraudulent websites, most of them use machine learning to classify and identify URLs and static files (HTML text and image resources) of web pages. These methods cannot take into account the text features contained in the pictures in the static files of the web page, and the content to be verified is too complex to recognize and classify the website quickly and accurately. In view of this, this system proposes a method combining SVM algorithm and improved website text features to quickly and efficiently identify and classify websites. Experimental results show that the system has excellent ability to identify and classify websites.

Citation Download Citation

Feng Zhou and Xiaodong Liu "Fraudulent website identification and classification system based on machine learning", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126452W (23 May 2023); https://doi.org/10.1117/12.2681459

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