{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T04:40:28Z","timestamp":1766378428215,"version":"3.37.3"},"reference-count":19,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2019,8,15]],"date-time":"2019-08-15T00:00:00Z","timestamp":1565827200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,12,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Proxies can help users to bypass the network filtering system, leaving the network open to banned content, and can also enable users to anonymize themselves for terminal security protection. Proxies are widely used in the current network environment. However, certain spy proxies record user information for privacy theft. In addition, attackers can use such technologies to anonymize malicious behaviors and hide identities. Such behaviors have posed serious challenges to the internal defense and security threat assessment of an organization; however, the anonymity of the proxy makes it consistent with normal network communication, and general network traffic identification methods are not able to detect it. To accurately and effectively discover proxy users in the organization based on s, a proxy user detection method based on communication behavior portrait offers the following: (1) analysis of the communication behavior from the perspective of the portrait. Based on not abandoning the effective information of the traffic itself, the label system is established by introducing exogenous data to identify the difference between proxy communication and normal communication. (2) Construction of the portrait feature set of proxy user detection based on the traffic file and external data by studying the differences between the attribute sets of communication behavior labels for proxy users and non-proxy users. (3) Design and implementation a data-driven machine learning method to supply guidance for automatic recognition of such behavior. The experimental results show that, compared with state-of-the-art methods, the detection accuracy for the proxy user exceeds 95%, and that of real network traffic environment exceeds 85%. These results indicate that the detection method proposed in this paper can accurately distinguish proxy communication and normal communication and thus achieves precise proxy user detection.<\/jats:p>","DOI":"10.1093\/comjnl\/bxz065","type":"journal-article","created":{"date-parts":[[2019,6,15]],"date-time":"2019-06-15T15:09:10Z","timestamp":1560611350000},"page":"1777-1792","source":"Crossref","is-referenced-by-count":7,"title":["Detecting Proxy User Based on Communication Behavior Portrait"],"prefix":"10.1093","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6607-928X","authenticated-orcid":false,"given":"Zhen-Hui","family":"Han","sequence":"first","affiliation":[{"name":"College of Cybersecurity, Sichuan University, Chengdu, Sichuan, China"},{"name":"Cybersecurity Research Institute, Sichuan University, Chengdu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing-Shu","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Cybersecurity, Sichuan University, Chengdu, Sichuan, China"},{"name":"Cybersecurity Research Institute, Sichuan University, Chengdu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue-Mei","family":"Zeng","sequence":"additional","affiliation":[{"name":"Cybersecurity Research Institute, Sichuan University, Chengdu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Zhu","sequence":"additional","affiliation":[{"name":"Cybersecurity Research Institute, Sichuan University, Chengdu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming-Yong","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Cybersecurity, Sichuan University, Chengdu, Sichuan, China"},{"name":"Cybersecurity Research Institute, Sichuan University, Chengdu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2019,8,15]]},"reference":[{"issue":"4","key":"2019121909072478900_ref1","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.1007\/s11277-013-1451-y","article-title":"Detecting anonymising proxy usage on the internet","volume":"75","author":"Oflaherty","year":"2014","journal-title":"Wireless Personal Communications"},{"key":"2019121909072478900_ref2","first-page":"153","volume-title":"World Congress on Internet Security. 2015 World Congress on Internet Security (WorldCIS)","author":"Miller","year":"2016"},{"key":"2019121909072478900_ref3","first-page":"82","volume-title":"2016 IEEE Conference on Communications and Network Security (CNS)","author":"Webb","year":"2017"},{"key":"2019121909072478900_ref4","first-page":"18","volume-title":"2015 IEEE 16th International Symposium on High Assurance Systems Engineering","author":"Foroushani","year":"2015"},{"key":"2019121909072478900_ref5","first-page":"75","volume-title":"2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)","author":"Ziye","year":"2017"},{"issue":"2","key":"2019121909072478900_ref6","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1016\/j.eswa.2009.06.059","article-title":"Neural networks-based detection of stepping-stone intrusion","volume":"37","author":"Wu","year":"2010","journal-title":"Expert Systems with Applications"},{"volume-title":"Research on ShadowSocks Anonymous Traffic Recognition Technology Based on Web Fingerprint","year":"2017","author":"Ruixing","key":"2019121909072478900_ref7"},{"volume-title":"Detecting and preventing anonymous proxy usage","year":"2008","author":"Brozycki","key":"2019121909072478900_ref8"},{"key":"2019121909072478900_ref9","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1109\/INFCOMW.2011.5928952","volume-title":"2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","author":"Lin","year":"2011"},{"key":"2019121909072478900_ref10","doi-asserted-by":"crossref","first-page":"1176","DOI":"10.1109\/ICICIC.2009.172","volume-title":"2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC)","author":"Hsiao","year":"2009"},{"key":"2019121909072478900_ref12","first-page":"1","volume-title":"2018 IEEE International Conference on Multimedia and Expo (ICME)","author":"Gu","year":"2018"},{"issue":"5","key":"2019121909072478900_ref13","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1002\/nem.1900","article-title":"Detecting malicious activities with user-agent-based profiles","volume":"25","author":"Zhang","year":"2015","journal-title":"International Journal of Network Management"},{"issue":"4","key":"2019121909072478900_ref14","first-page":"738","article-title":"Identification of cyber criminal by analysing the users profile","volume":"20","author":"Veena","year":"2018","journal-title":"I. 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