Year: 2025
Type: article
Abstract: In order to reduce the probability of website users being attacked and maintain the safety of website operation, this study proposes an automatic vulnerability detection method of websites based on associated data. We use plug-ins to scan the website in all directions, establish a scanning database, and classify and store the scanned web data. By applying optimized an a priori association rule algorithm, key features are extracted from web scan data, which are then transformed into input samples for a K-means clustering algorithm. The aim is to efficiently extract feature attributes of website vulnerability data and ultimately construct a text vectorized representation of vulnerability data. Convolutional neural networks can automatically detect website vulnerabilities by using the constructed text vector as input. Experimental verification shows that this method demonstrates comprehensive data coverage, efficient processing speed, and high-precision recognition performance. It not only significantly reduces the clustering analysis time, but also ensures the accuracy and timeliness of vulnerability detection.
Source: Journal of Web Engineering
Authors: Xiaoli Li, Ling Zhao, Haobin Shen, Hanlin Du, Zhida Guo
Institution: Guangdong Power Grid Company (China)
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409716006