Please wait a minute...
 首页  期刊介绍 期刊订阅 联系我们 横山亮次奖 百年刊庆
 
最新录用  |  预出版  |  当期目录  |  过刊浏览  |  阅读排行  |  下载排行  |  引用排行  |  横山亮次奖  |  百年刊庆
清华大学学报(自然科学版)  2021, Vol. 61 Issue (11): 1254-1259    DOI: 10.16511/j.cnki.qhdxxb.2021.25.003
  漏洞分析与风险评估 本期目录 | 过刊浏览 | 高级检索 |
基于跨域资源访问的浏览器用户追踪
宋宇波1,2, 吴天琦1,2, 胡爱群2,3, 高尚4
1. 东南大学 网络空间安全学院, 江苏省计算机网络技术重点实验室, 南京 211189;
2. 网络通信与安全紫金山实验室, 南京 211189;
3. 东南大学 信息科学与工程学院, 移动通信国家重点实验室, 南京 211189;
4. 香港理工大学 电子计算学系, 香港 999077
Browser user tracking based on cross-domain resource access
SONG Yubo1,2, WU Tianqi1,2, HU Aiqun2,3, GAO Shang4
1. Jiangsu Key Laboratory of Computer Networking Technology, School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China;
2. Purple Mountain Laboratories, Nanjing 211189, China;
3. National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing 211189, China;
4. Computing Department, Hong Kong Polytheistic University, Hong Kong 999077, China
全文: PDF(2713 KB)   HTML
输出: BibTeX | EndNote (RIS)      
摘要 近年来,点击欺诈给广告商造成了巨大的经济损失,迫使广告商支付高额费用。为了应对点击欺诈,广告商通常使用用户配置文件来识别用户身份。但是,攻击者可以轻松构建独特的虚拟操作环境,干扰身份的识别。该文提出了PingLoc机制,一种基于跨域资源访问的定位方案,可检测点击欺诈来源。该方案从ping响应延迟序列提取特征构建用户指纹。测试表明,PingLoc所收集的延迟特征是稳定的,用户定位指纹的准确性高达98%。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
宋宇波
吴天琦
胡爱群
高尚
关键词 点击欺诈多点ping用户身份识别攻击者定位    
Abstract:In recent years, click fraud has caused huge economic losses to advertisers. Many advertisers have then used "user profiles" to identify users to eliminate click fraud. However, attackers can easily construct unique virtual operating environments to confuse the identification algorithms. This paper introduces a localization scheme to detect click fraud sources based on cross-domain resource access. This scheme extracts features from a ping response delay series to fingerprint users. Tests show that the delay features collected by this method are stable with a fingerprint localization accuracy of up to 98%.
Key wordsclick fraud    multilocalization pings    user identification    attacker localization
收稿日期: 2020-11-15      出版日期: 2021-10-19
基金资助:国家重点研发计划项目(2020YFE0200600);江苏省网络与信息安全重点实验室(BM2003201)
引用本文:   
宋宇波, 吴天琦, 胡爱群, 高尚. 基于跨域资源访问的浏览器用户追踪[J]. 清华大学学报(自然科学版), 2021, 61(11): 1254-1259.
SONG Yubo, WU Tianqi, HU Aiqun, GAO Shang. Browser user tracking based on cross-domain resource access. Journal of Tsinghua University(Science and Technology), 2021, 61(11): 1254-1259.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2021.25.003  或          http://jst.tsinghuajournals.com/CN/Y2021/V61/I11/1254
  
  
  
  
  
  
[1] ZHANG X, LIU X J, GUO H. A click fraud detection scheme based on cost sensitive BPNN and ABC in mobile advertising[C]//2018 IEEE 4th International Conference on Computer and Communications (ICCC). Chengdu, China:IEEE, 2018.
[2] GUO Y, SHI J Z, CAO Z G, et al. Machine learning based cloudbot detection using multi-layer traffic statistics[C]//2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). Zhangjiajie, China:IEEE, 2019.
[3] LAPERDRIX P, AVOINE G, BAUDRY B, et al. Morellian analysis for browsers:Making web authentication stronger with canvas fingerprinting[C]//Proceedings of the 16th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment. Gothenburg:Springer, 2019:43-66.
[4] ACAR G, EUBANK C, ENGLEHARDT S, et al. The web never forgets:Persistent tracking mechanisms in the wild[C]//Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security. Arizona, Scottsdale, USA:Association for Computing Machinery, 2014:674-689.
[5] QUEIROZ J S, FEITOSA E L. A web browser fingerprinting method based on the Web audio API[J]. The Computer Journal, 2019, 62(8):1106-1120.
[6] ENGLEHARDT S, NARAYANAN A. Online tracking:A 1-million-site measurement and analysis[C]//Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. Vienna, Austria:Association for Computing Machinery, 2016:1388-1401.
[7] ABOUOLLO A, ALMUHAMMADI S. Detecting malicious user accounts using canvas fingerprint[C]//2017 8th International Conference on Information and Communication Systems. Irbid, Jordan:IEEE, 2017.
[8] ALSWITI W, ALQATAWNA J, AL-SHBOUL B, et al. Users profiling using clickstream data analysis and classification[C]//2016 Cybersecurity and Cyberforensics Conference (CCC). Amman, Jordan:IEEE, 2016:96-99.
[9] LI X Y, CUI X, SHI L M, et al. Constructing browser fingerprint tracking chain based on LSTM model[C]//2018 IEEE Third International Conference on Data Science in Cyberspace (DSC). Guangzhou, China:IEEE, 2018:213-218.
[10] CAO Y Z, LI S, WIJMANS E. (Cross-)browser fingerprinting via OS and hardware level features[C]//Network and Distributed System Security Symposium. San Diego, USA, 2017.
[11] CHEN J J, JIANG J, DUAN H X, et al. We still don't have secure cross-domain requests:An empirical study of CORS[C]//27th USENIX Security Symposium (USENIX Security 18). Baltimore, MD:USENIX Association, 2018:1079-1093.
[12] MIRSKY Y, KALBO N, ELOVICI Y, et al. Vesper:Using echo analysis to detect man-in-the-middle attacks in LANs[J]. IEEE Transactions on Information Forensics and Security, 2019, 14(6):1638-1653.
[13] ABDOU A M, MATRAWY A, VAN OORSCHOT P C. Location verification on the internet:Towards enforcing location-aware access policies over internet clients[C]//2014 IEEE Conference on Communications and Network Security. San Francisco, USA:IEEE, 2014:175-183.
No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 《清华大学学报(自然科学版)》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn