微型电脑应用

为了高效发现网络系统中的安全漏洞，提出一种基于模糊测试的方法。利用网络态势感知模型识别潜在漏洞，通过广度优先网络爬虫获取数据后，利用关联规则学习算法进行挖掘。构造模糊测试输入数据，提取漏洞特征并经自适应聚类处理，构建漏洞关联挖掘模型。实验表明，所提方法提供直观可视化结果，实现高速率漏洞挖掘，保障网络安全。网络传输速度提升至60 MB/s以上，平均网络中断时间降至4.6 min,用户体验显著改善。

To efficiently discover security vulnerabilities in network systems, a method is proposed based on fuzzy testing. A network situational awareness model is used to identify potential vulnerabilities, data are obtained through breadth first Web crawlers, and association rule learning algorithms are utilized to mine data. Fuzzy test input data are constructed, vulnerability features are extracted and adaptive clustering processing is performed to construct a vulnerability association mining model. The experiment shows that the proposed method provides intuitive visualization results, achieves high-speed vulnerability mining, and ensures network security. The network transmission speed increases to over 60 MB/s, the average network interruption time reduces to 4.6 min, and the user experience is significantly improved.