Abstract:

With the rapid development of internet technology, cybersecurity threats have become increasingly severe. Malicious websites, serving as primary carriers of cyberattacks, pose significant threats to user information security and digital asset safety through phishing scams, malware distribution, and other means. The purpose of this paper was to enhance the accuracy of malicious websites assessment, taking malicious websites as the research object, covering the research scope of multi-dimensional feature analysis and PageRank algorithm optimization. This study employed various research methods and theories, including domain name feature analysis, registration information inquiry, domain name inclusion search, traffic behavior analysis, content quality assessment, user behavior data collection, and time decay factor integration. This paper combined natural language processing technology, machine learning algorithms and time decay mechanisms, proposed a comprehensive malicious websites assessment system and verified its effectiveness in improving the accuracy of malicious websites assessment. Experimental results demonstrate that this method achieves a comprehensive accuracy rate to 99.99%, which represents a significant improvement over traditional methods. The research findings presented in this paper provide robust support for cybersecurity protection and remain significant for constructing a safer and more trustworthy online environment.

Key words: malicious websites assessment, PageRank algorithm, content quality assessment, user behavior data, time decay factor