Loading…
User Authentication from Web Browsing Behavior
As anticipated in True Names by Vernor Vinge, identity has been recognized as our most valued possession in cyberspace. Attribution is a key concept in enabling trusted identities and deterring malicious activities. As more people use the Web to communicate, work, and otherwise have fun, is it possi...
Saved in:
Main Authors: | , |
---|---|
Format: | Report |
Language: | English |
Subjects: | |
Online Access: | Request full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | |
container_end_page | |
container_issue | |
container_start_page | |
container_title | |
container_volume | |
creator | Abramson, Myriam Aha, David W |
description | As anticipated in True Names by Vernor Vinge, identity has been recognized as our most valued possession in cyberspace. Attribution is a key concept in enabling trusted identities and deterring malicious activities. As more people use the Web to communicate, work, and otherwise have fun, is it possible to uniquely identify someone based on their Web browsing behavior or to differentiate between two persons based solely on their Web browsing histories? Based on a user study, this paper provides some insights into these questions. We describe characteristic features of Web browsing behavior and present our algorithm and analysis of an ensemble learning approach leveraging from those features for user authentication.
Presented at the 2013 Florida Artificial Intelligence Research Society Conference held in St. Pete Beach, FL on 22-24 May 2013. |
format | report |
fullrecord | <record><control><sourceid>dtic_1RU</sourceid><recordid>TN_cdi_dtic_stinet_ADA599778</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>ADA599778</sourcerecordid><originalsourceid>FETCH-dtic_stinet_ADA5997783</originalsourceid><addsrcrecordid>eNrjZNALLU4tUnAsLclIzSvJTE4syczPU0grys9VCE9NUnAqyi8vzsxLV3BKzUgsy8wv4mFgTUvMKU7lhdLcDDJuriHOHropQM3xxSWZeakl8Y4ujqaWlubmFsYEpAFKJye3</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>report</recordtype></control><display><type>report</type><title>User Authentication from Web Browsing Behavior</title><source>DTIC Technical Reports</source><creator>Abramson, Myriam ; Aha, David W</creator><creatorcontrib>Abramson, Myriam ; Aha, David W ; NAVAL RESEARCH LAB WASHINGTON DC</creatorcontrib><description>As anticipated in True Names by Vernor Vinge, identity has been recognized as our most valued possession in cyberspace. Attribution is a key concept in enabling trusted identities and deterring malicious activities. As more people use the Web to communicate, work, and otherwise have fun, is it possible to uniquely identify someone based on their Web browsing behavior or to differentiate between two persons based solely on their Web browsing histories? Based on a user study, this paper provides some insights into these questions. We describe characteristic features of Web browsing behavior and present our algorithm and analysis of an ensemble learning approach leveraging from those features for user authentication.
Presented at the 2013 Florida Artificial Intelligence Research Society Conference held in St. Pete Beach, FL on 22-24 May 2013.</description><language>eng</language><subject>ALGORITHMS ; BEHAVIOR ; Computer Systems Management and Standards ; IDENTITIES ; INTERNET ; INTERNET BROWSERS ; LEARNING ; Numerical Mathematics ; USER AUTHENTICATION ; WEB BROWSING BEHAVIOR</subject><creationdate>2013</creationdate><rights>Approved for public release; distribution is unlimited.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,780,885,27567,27568</link.rule.ids><linktorsrc>$$Uhttps://apps.dtic.mil/sti/citations/ADA599778$$EView_record_in_DTIC$$FView_record_in_$$GDTIC$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Abramson, Myriam</creatorcontrib><creatorcontrib>Aha, David W</creatorcontrib><creatorcontrib>NAVAL RESEARCH LAB WASHINGTON DC</creatorcontrib><title>User Authentication from Web Browsing Behavior</title><description>As anticipated in True Names by Vernor Vinge, identity has been recognized as our most valued possession in cyberspace. Attribution is a key concept in enabling trusted identities and deterring malicious activities. As more people use the Web to communicate, work, and otherwise have fun, is it possible to uniquely identify someone based on their Web browsing behavior or to differentiate between two persons based solely on their Web browsing histories? Based on a user study, this paper provides some insights into these questions. We describe characteristic features of Web browsing behavior and present our algorithm and analysis of an ensemble learning approach leveraging from those features for user authentication.
Presented at the 2013 Florida Artificial Intelligence Research Society Conference held in St. Pete Beach, FL on 22-24 May 2013.</description><subject>ALGORITHMS</subject><subject>BEHAVIOR</subject><subject>Computer Systems Management and Standards</subject><subject>IDENTITIES</subject><subject>INTERNET</subject><subject>INTERNET BROWSERS</subject><subject>LEARNING</subject><subject>Numerical Mathematics</subject><subject>USER AUTHENTICATION</subject><subject>WEB BROWSING BEHAVIOR</subject><fulltext>true</fulltext><rsrctype>report</rsrctype><creationdate>2013</creationdate><recordtype>report</recordtype><sourceid>1RU</sourceid><recordid>eNrjZNALLU4tUnAsLclIzSvJTE4syczPU0grys9VCE9NUnAqyi8vzsxLV3BKzUgsy8wv4mFgTUvMKU7lhdLcDDJuriHOHropQM3xxSWZeakl8Y4ujqaWlubmFsYEpAFKJye3</recordid><startdate>201305</startdate><enddate>201305</enddate><creator>Abramson, Myriam</creator><creator>Aha, David W</creator><scope>1RU</scope><scope>BHM</scope></search><sort><creationdate>201305</creationdate><title>User Authentication from Web Browsing Behavior</title><author>Abramson, Myriam ; Aha, David W</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-dtic_stinet_ADA5997783</frbrgroupid><rsrctype>reports</rsrctype><prefilter>reports</prefilter><language>eng</language><creationdate>2013</creationdate><topic>ALGORITHMS</topic><topic>BEHAVIOR</topic><topic>Computer Systems Management and Standards</topic><topic>IDENTITIES</topic><topic>INTERNET</topic><topic>INTERNET BROWSERS</topic><topic>LEARNING</topic><topic>Numerical Mathematics</topic><topic>USER AUTHENTICATION</topic><topic>WEB BROWSING BEHAVIOR</topic><toplevel>online_resources</toplevel><creatorcontrib>Abramson, Myriam</creatorcontrib><creatorcontrib>Aha, David W</creatorcontrib><creatorcontrib>NAVAL RESEARCH LAB WASHINGTON DC</creatorcontrib><collection>DTIC Technical Reports</collection><collection>DTIC STINET</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Abramson, Myriam</au><au>Aha, David W</au><aucorp>NAVAL RESEARCH LAB WASHINGTON DC</aucorp><format>book</format><genre>unknown</genre><ristype>RPRT</ristype><btitle>User Authentication from Web Browsing Behavior</btitle><date>2013-05</date><risdate>2013</risdate><abstract>As anticipated in True Names by Vernor Vinge, identity has been recognized as our most valued possession in cyberspace. Attribution is a key concept in enabling trusted identities and deterring malicious activities. As more people use the Web to communicate, work, and otherwise have fun, is it possible to uniquely identify someone based on their Web browsing behavior or to differentiate between two persons based solely on their Web browsing histories? Based on a user study, this paper provides some insights into these questions. We describe characteristic features of Web browsing behavior and present our algorithm and analysis of an ensemble learning approach leveraging from those features for user authentication.
Presented at the 2013 Florida Artificial Intelligence Research Society Conference held in St. Pete Beach, FL on 22-24 May 2013.</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | eng |
recordid | cdi_dtic_stinet_ADA599778 |
source | DTIC Technical Reports |
subjects | ALGORITHMS BEHAVIOR Computer Systems Management and Standards IDENTITIES INTERNET INTERNET BROWSERS LEARNING Numerical Mathematics USER AUTHENTICATION WEB BROWSING BEHAVIOR |
title | User Authentication from Web Browsing Behavior |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T13%3A01%3A40IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-dtic_1RU&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=unknown&rft.btitle=User%20Authentication%20from%20Web%20Browsing%20Behavior&rft.au=Abramson,%20Myriam&rft.aucorp=NAVAL%20RESEARCH%20LAB%20WASHINGTON%20DC&rft.date=2013-05&rft_id=info:doi/&rft_dat=%3Cdtic_1RU%3EADA599778%3C/dtic_1RU%3E%3Cgrp_id%3Ecdi_FETCH-dtic_stinet_ADA5997783%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |