一种车联网环境下的多阶段恶意网站快速检测方法OA

The number of malicious websites accessed by in-vehicle third-party services has been rapidly increasing,posing a significant threat to the security of the Internet of Vehicles(IoV).Currently,three major challenges exist for IoV malicious website detection:traditional tools exhibit high detection latency when processing large-scale website data,the presence of obfuscated malicious Uniform Resource Locators(URLs)reduces detection accuracy,and the difficulty in obtaining malicious website datasets further hinders effective detection.Collectively,these factors limit the efficiency and accuracy of detection.To address these issues,this study proposes a fast multi-stage malicious website detection method based on logistic regression.This method uses search engines for the preliminary filtering of legitimate websites to reduce computational resource wastage.It designs matching rules through the analysis and summarization of malicious obfuscation techniques and introduces a heuristic rule-based malicious website filtering method to effectively filter obfuscated website URLs,overcoming the limitations of traditional tools in detecting URLs with malicious obfuscation.To further enhance the detection accuracy,it constructs a comprehensive and lightweight set of malicious website detection features and employs logistic regression classification for feature extraction and analysis.Experimental results demonstrate that the proposed method significantly outperforms traditional methods in terms of accuracy and efficiency in malicious website detection,achieving an accuracy of 98.1％on public datasets and reducing the detection time by approximately 75％.