| 基于KNN和GBDT的Web服务器指纹识别技术 |
| |
| 引用本文： | 南世慧,魏伟,吴华清,邹金蓉,赵志文.基于KNN和GBDT的Web服务器指纹识别技术[J].计算机科学,2018,45(8):141-145. |
| |
| 作者姓名： | 南世慧 魏伟 吴华清 邹金蓉 赵志文 |
| |
| 作者单位： | 北京师范大学研究生院珠海分院 广东 珠海519087,北京师范大学信息科学与技术学院 北京100875,北京师范大学研究生院珠海分院 广东 珠海519087,北京师范大学信息科学与技术学院 北京100875,北京师范大学研究生院珠海分院 广东 珠海519087;北京师范大学信息科学与技术学院 北京100875 |
| |
| 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。
|
| 关 键 词： | Web指纹 梯度提升决策树 集成学习 网络安全 |
| 收稿时间： | 2017/5/9 0:00:00 |
| 修稿时间： | 2017/10/13 0:00:00 |
Web Server Fingerprint Identification Technology Based on KNN and GBDT |
| |
| NAN Shi-hui,WEI Wei,WU Hua-qing,ZOU Jing-rong and ZHAO Zhi-wen.Web Server Fingerprint Identification Technology Based on KNN and GBDT[J].Computer Science,2018,45(8):141-145. |
| |
| Authors: | NAN Shi-hui WEI Wei WU Hua-qing ZOU Jing-rong and ZHAO Zhi-wen |
| |
| Affiliation: | Zhuhai Branch,Graduate School of Beijing Normal University,Zhuhai,Guangdong 519087,China,School of Information Science and Technology,Beijing Normal University,Beijing 100875,China,Zhuhai Branch,Graduate School of Beijing Normal University,Zhuhai,Guangdong 519087,China,School of Information Science and Technology,Beijing Normal University,Beijing 100875,China and Zhuhai Branch,Graduate School of Beijing Normal University,Zhuhai,Guangdong 519087,China;School of Information Science and Technology,Beijing Normal University,Beijing 100875,China |
| |
| Abstract: | 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. |
| |
| Keywords: | Web fingerprint Gradient decision boosting tree Ensemble learning Cyber security |
|
|
点击此处可从《计算机科学》下载全文 |
|
