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The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services (CEC/EEE'06)
Detecting Profile Injection Attacks in Collaborative Recommender Systems
San Francisco, CA
June 26-June 29
ISBN: 0-7695-2511-3
| ASCII Text | x | ||
| Robin Burke, Bamshad Mobasher, Chad Williams, Runa Bhaumik, "Detecting Profile Injection Attacks in Collaborative Recommender Systems," E-Commerce Technology, IEEE International Conference on, and Enterprise Computing, E-Commerce, and E-Services, IEEE International Conference on, pp. 23, The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services (CEC/EEE'06), 2006. | |||
| BibTex | x | ||
| @article{ 10.1109/CEC-EEE.2006.34, author = {Robin Burke and Bamshad Mobasher and Chad Williams and Runa Bhaumik}, title = {Detecting Profile Injection Attacks in Collaborative Recommender Systems}, journal ={E-Commerce Technology, IEEE International Conference on, and Enterprise Computing, E-Commerce, and E-Services, IEEE International Conference on}, volume = {0}, year = {2006}, isbn = {0-7695-2511-3}, pages = {23}, doi = {http://doi.ieeecomputersociety.org/10.1109/CEC-EEE.2006.34}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - E-Commerce Technology, IEEE International Conference on, and Enterprise Computing, E-Commerce, and E-Services, IEEE International Conference on TI - Detecting Profile Injection Attacks in Collaborative Recommender Systems SN - 0-7695-2511-3 SP EP A1 - Robin Burke, A1 - Bamshad Mobasher, A1 - Chad Williams, A1 - Runa Bhaumik, PY - 2006 KW - null VL - 0 JA - E-Commerce Technology, IEEE International Conference on, and Enterprise Computing, E-Commerce, and E-Services, IEEE International Conference on ER - | |||
Collaborative recommender systems are known to be highly vulnerable to profile injection attacks, attacks that involve the insertion of biased profiles into the ratings database for the purpose of altering the system?s recommendation behavior. In prior work, we and others have identified a number of models for such attacks and shown their effectiveness. This paper describes a classification approach to the problem of detecting and responding to profile injection attacks. This technique significantly reduces the effectiveness of the most powerful attack models previously studied.
Citation:
Robin Burke, Bamshad Mobasher, Chad Williams, Runa Bhaumik, "Detecting Profile Injection Attacks in Collaborative Recommender Systems," cec-eee, pp.23, The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services (CEC/EEE'06), 2006
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