摘要
现有的Web服务器指纹识别方法容易因响应头被篡改而得不到准确的识别结果,而且已有的基于机器学习的相关识别方法需要预先发送大量的请求来进行识别。针对上述问题,通过分析响应头的特征关系,提出一种基于KNN和GBDT的Web服务器指纹识别算法,其只需要发送两种不同类型的异常请求,就能识别对应的Web服务器指纹类型和版本范围。与已有Web服务器指纹识别算法进行的对比实验结果表明,所提算法的识别速度和准确率均得到了优化。
Conventional Web server fingerprinting method is easy to modify the response head so that the recognition result is not accurate,and the existing recognition method based on machine learning needs to send a large number of requests for identification.To solve these problems,by analyzing the feature relations of the response head,a Web server fingerprint recognition algorithm based on KNN and GBDT was proposed.Only two different types of exception requests are sent to identify the corresponding Web server fingerprint type and version range.Compared with the existing algorithm of the relevant Web server fingerprint recognition,the proposed algorithm can optimize the recognition speed and the recognition accuracy.
作者
南世慧
魏伟
吴华清
邹金蓉
赵志文
NAN Shi-hui;WEI Wei;WU Hua-qing;ZOU Jing-rong;ZHAO Zhi-wen(Zhuhai Branch,Graduate School of Beijing Normal University,Zhuhai,Guangdong 519087,China;School of Information Science and Technology,Beijing Normal University,Beijing 100875,China)
出处
《计算机科学》
CSCD
北大核心
2018年第8期141-145,共5页
Computer Science
关键词
Web指纹
梯度提升决策树
集成学习
网络安全
Web fingerprint
Gradient decision boosting tree
Ensemble learning
Cyber security
