[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126805-en":3,"doc-seo-126805-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126805,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","OVERCOMING TOR - AN ANALYSIS OF VIDEO FINGERPRINTING ATTACKS WITH MACHINE LEARNING","Terrorist and violent extremist organizations exploit the anonymity of The Onion Router (Tor) to clandestinely distribute video for recruitment, propaganda dissemination, and incitement to terrorism. This thesis advances machine learning methods for performing video fingerprinting attacks against network traffic transmitted over Tor in a closed world setting. Building on prior work, it extends evaluation across multiple streaming media platforms. The research aims to support development of an operational capability for the Department of Defense to identify individuals using Tor and the dark web for recruitment, training, and terror incitement, as well as to detect people actively being recruited.","Calhoun: The NPS Institutional Archive  \nDSpace Repository  \n\n| 2023-09\u003Cbr>OVERCOMING TOR: AN ANALYSIS OF VIDEO\u003Cbr>FINGERPRINTING ATTACKS WITH MACHINE LEARNING\u003Cbr>Thomas, Trevor J.\u003Cbr>Monterey, CA; Naval Postgraduate School |\n| --- |\n| [https://hdl.handle.net/10945/72401](https://hdl.handle.net/10945/72401) |\n\nNPS Scholarship Theses  \nThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.  \nDownloaded from NPS Archive: Calhoun  \nNAVAL POSTGRADUATE  \nSCHOOL MONTEREY, CALIFORNIA  \nTHESIS  \nOVERCOMING TOR: AN ANALYSIS OF VIDEO FINGERPRINTING ATTACKS WITH MACHINE LEARNING  \nby  \nTrevor J. Thomas  \nSeptember 2023  \nThesis Advisor: Armon C. Barton  \nSecond Reader: Geoffrey G. Xie  \nApproved for public release. Distribution is unlimited.  \nTHIS PAGE INTENTIONALLY LEFT BLANK  \n\n| REPORT DOCUMENTATION PAGE |  |  |  |  | Form Approved OMB No. 0704-0188 |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington, DC 20503. |  |  |  |  |  |  |  |\n| 1. AGENCY USE ONLY (Leave blank) |  | 2. REPORT DATE\u003Cbr>September 2023 |  | 3. REPORT TYPE AND DATES COVERED\u003Cbr>Master's thesis |  |  |  |\n| 4. TITLE AND SUBTITLE\u003Cbr>OVERCOMING TOR: AN ANALYSIS OF VIDEO FINGERPRINTING ATTACKS WITH MACHINE LEARNING |  |  |  |  |  | 5. FUNDING NUMBERS |  |\n| 6. AUTHOR(S) Trevor J. Thomas |  |  |  |  |  |  |  |\n| 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)\u003Cbr>Naval Postgraduate School\u003Cbr>Monterey, CA 93943-5000 |  |  |  |  |  | 8. PERFORMING\u003Cbr>ORGANIZATION REPORT NUMBER |  |\n| 9. SPONSORING / MONITORING AGENCY NAME(S) AND\u003Cbr>ADDRESS(ES)\u003Cbr>N/A |  |  |  |  |  | 10. SPONSORING / MONITORING AGENCY REPORT NUMBER |  |\n| 11. SUPPLEMENTARY NOTES The views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. |  |  |  |  |  |  |  |\n| 12a. DISTRIBUTION / AVAILABILITY STATEMENT\u003Cbr>Approved for public release. Distribution is unlimited. |  |  |  |  |  | 12b. DISTRIBUTION CODE\u003Cbr>A |  |\n| 13. ABSTRACT (maximum 200 words)\u003Cbr>Terrorist groups and violent extremist organizations have taken advantage of the anonymity provided by The Onion Router (Tor) . They use it to clandestinely distribute video media to recruit and spread propaganda as well as incite others to commit acts of terrorism. This thesis seeks to continue a line of work in developing machine learning algorithms to conduct video fingerprinting attacks on network traffic transmitted over Tor in a closed world environment. It will expand upon previous research by using multiple media platforms for videos being streamed. The end goal from this line of research is a future product that the Department of Defense can apply against real world network traffic to identify individuals using Tor and the dark web for recruiting, training, and incitement of terror as well as identify individuals that are actively being recruited. |  |  |  |  |  |  |  |\n| 14. SUBJECT TERMS\u003Cbr>video fingerprinting, AI, TOR, artificial intelligence, The Onion Router |  |  |  |  |  |  | 15. NUMBER OF PAGES\u003Cbr>49 |\n|  |  |  |  |  |  |  | 16. PRICE CODE |\n| 17. SECURITY\u003Cbr>CLASSIFICATION OF REPORT\u003Cbr>Unclassified | 18. SECURITY\u003Cbr>CLASSIFICATION OF TH","cbCairQP2veOhg41","https://ap.wps.com/l/cbCairQP2veOhg41","pdf",2671568,1,51,"English","en",105,"# Introduction\n## Purpose and Scope\n## Thesis Organization\n# Background and Related Work","[{\"question\":\"What problem does the thesis address regarding Tor and video distribution?\",\"answer\":\"It examines how extremist groups use Tor’s anonymity to distribute video covertly for recruitment, propaganda, and incitement to terrorism.\"},{\"question\":\"What technical approach does the thesis develop?\",\"answer\":\"It focuses on creating machine learning algorithms for video fingerprinting attacks on Tor-transmitted network traffic in a closed world environment.\"},{\"question\":\"How does the thesis extend previous research and what is the intended end goal?\",\"answer\":\"It expands testing by using multiple media platforms for streamed videos, aiming to enable a future Department of Defense capability to identify recruiters and actively recruited individuals on Tor and the dark web.\"}]","OVERCOMING TOR - 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