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@INPROCEEDINGS{BWMB06,
  author = {Runa Bhaumik and Chad Williams and Bamshad Mobasher and Robin Burke},
  title = {Securing Collaborative Filtering Against Malicious Attacks Through
	Anomaly Detection},
  booktitle = {Proceedings of the 4th Workshop on Intelligent Techniques for Web
	Personalization (ITWP'06)},
  year = {2006},
  address = {Held at AAAI 2006, Boston, Massachusetts},
  month = {July},
  owner = {Chad Williams},
  timestamp = {2008.02.26}
}

@INPROCEEDINGS{BMBW05,
  author = {R. Burke and B. Mobasher and R. Bhaumik and C. Williams},
  title = {Segment-Based Injection Attacks against Collaborative Filtering Recommender
	Systems},
  booktitle = {Proceedings of the 2005 International Conference on Data Mining (ICDM'05)},
  year = {2005},
  address = {Houston, Texas},
  month = {November},
  owner = {Chad Williams},
  timestamp = {2008.02.26}
}

@INPROCEEDINGS{BMBW05b,
  author = {R. Burke and B. Mobasher and R. Bhaumik and C. Williams},
  title = {Collaborative Recommendation Vulnerability to Focused Bias Injection
	Attacks},
  booktitle = {Proceedings of the Workshop on Privacy and Security Aspects of Data
	Mining},
  year = {2005},
  address = {Proceedings of the Workshop on Privacy and Security Aspects of Data
	Mining
	
	Held at ICDM'05, Houston, Texas},
  month = {November},
  owner = {Chad Williams},
  timestamp = {2008.02.26}
}

@INPROCEEDINGS{BMWB06,
  author = {Robin Burke and Bamshad Mobasher and Chad Williams and Runa Bhaumik},
  title = {Classification features for attack detection in collaborative recommender
	systems},
  booktitle = {KDD '06: Proceedings of the 12th ACM SIGKDD international conference
	on Knowledge discovery and data mining},
  year = {2006},
  pages = {542--547},
  address = {New York, NY, USA},
  publisher = {ACM},
  doi = {http://doi.acm.org/10.1145/1150402.1150465},
  isbn = {1-59593-339-5},
  location = {Philadelphia, PA, USA},
  owner = {Chad Williams},
  timestamp = {2008.02.26}
}

@INPROCEEDINGS{BMWB06b,
  author = {Robin Burke and Bamshad Mobasher and Chad Williams and Runa Bhaumik},
  title = {Detecting Profile Injection Attacks in Collaborative Recommender
	Systems},
  booktitle = {Proceedings of the 8th IEEE Conference on E-Commerce Technology (CEC'
	06)},
  year = {2006},
  address = {San Francisco, California},
  month = {June},
  owner = {Chad Williams},
  timestamp = {2008.02.26}
}

@ARTICLE{MBBW07,
  author = {Bamshad Mobasher and Robin Burke and Runa Bhaumik and Chad Williams},
  title = {Toward trustworthy recommender systems: An analysis of attack models
	and algorithm robustness},
  journal = {ACM Trans. Inter. Tech.},
  year = {2007},
  volume = {7},
  pages = {23},
  number = {4},
  address = {New York, NY, USA},
  doi = {http://doi.acm.org/10.1145/1278366.1278372},
  issn = {1533-5399},
  owner = {Chad Williams},
  publisher = {ACM},
  timestamp = {2008.02.26}
}

@INPROCEEDINGS{MBBW05,
  author = {B. Mobasher and R. Burke and R. Bhaumik and C. Williams},
  title = {Effective Attack Models for Shilling Item-Based Collaborative Filtering
	Systems},
  booktitle = {Proceedings of the 2005 WebKDD Workshop},
  year = {2005},
  address = {Held at KDD'2005, Chicago, Illinois},
  month = {August},
  owner = {Chad Williams},
  timestamp = {2008.02.26}
}

@INCOLLECTION{MBWB06,
  author = {B. Mobasher and R. Burke and C. Williams and R. Bhaumik},
  title = {Analysis and Detection of Segment-Focused Attacks Against Collaborative
	Recommendation},
  booktitle = {Web Mining and Web Usage Analysis},
  publisher = {Springer Berlin Heidelberg},
  year = {2006},
  editor = {O. Nasraoui, O. R. Zaļane, M. Spiliopoulou, B. Mobasher, B. Masand,
	and P. S. Yu},
  volume = {4198},
  series = {Lecture Notes in Artificial Intelligence},
  pages = {96-118},
  owner = {Chad Williams},
  timestamp = {2008.02.26}
}

@TECHREPORT{WMND07,
  author = {Chad A. Williams and Abolfazl Mohammadian and Peter C. Nelson and
	Sean T. Doherty},
  title = {Mining Sequential Association Rules For Traveler Context Prediction},
  institution = {Department of Computer Science, University of Illinois at Chicago},
  year = {2007},
  number = {2007.08.01-001},
  owner = {Chad Williams},
  timestamp = {2008.03.02}
}

@INPROCEEDINGS{WBRM06,
  author = {Chad Williams and Runa Bhaumik and Robin Burke and Bamshad Mobasher},
  title = {The Impact of Attack Profile Classification on the Robustness of
	Collaborative Recommendation},
  booktitle = {Proceedings of the 2006 WebKDD Workshop},
  year = {2006},
  address = {Held at KDD'2006, Philadelphia},
  month = {August},
  owner = {Chad Williams},
  timestamp = {2008.02.26}
}

@INPROCEEDINGS{WBSMB06,
  author = {Chad Williams and Runa Bhaumik and Jeff Sandvig and Bamshad Mobasher
	and Robin Burke},
  title = {Evaluation of Profile Injection Attacks In Collaborative Recommender
	Systems},
  booktitle = {DePaul CTI Research Symposium / Midwest Software Engineering Conference
	(CTIRS/MSEC 2006)},
  year = {2006},
  address = {Chicago, Illinois},
  month = {April},
  owner = {Chad Williams},
  timestamp = {2008.05.30}
}

@MASTERSTHESIS{W06,
  author = {Chad Williams and Bamshad Mobasher},
  title = {Profile Injection Attack Detection for Securing Collaborative Recommender
	Systems},
  school = {Department of Computer Science, DePaul University},
  year = {2006},
  type = {Department of Computer Science Technical Report No. 06-014},
  abstract = {Researchers have shown that collaborative recommender systems, the
	most common type of web personalization system, are highly vulnerable
	to attack. Attackers can use automated means to inject a large number
	of biased profiles into such a system, resulting in recommendations
	that favor or disfavor given items. Since collaborative recommender
	systems must be open to user input, it is difficult to design a system
	that cannot be so attacked. Researchers studying robust recommendation
	have therefore begun to study mechanisms for recognizing and defeating
	attacks. In prior work, we have introduced a variety of attributes
	designed to detect profile injection attacks and evaluated their
	combined classification performance against several well studied
	attack models using supervised classification techniques. In this
	paper, we propose and study the impact the dimensions of attack type,
	attack intent, filler size, and attack size have on the effectiveness
	of such a detection scheme. We conclude by experimentally exploring
	the weaknesses of a detection scheme based on supervised classification,
	and techniques that can be combined with this approach to address
	these vulnerabilities.},
  owner = {Chad Williams},
  timestamp = {2008.03.02},
  url = {http://facweb.cti.depaul.edu/research/TechReports/abstract06014.htm}
}

@ARTICLE{WMB07,
  author = {Williams, Chad and Mobasher, Bamshad and Burke, Robin},
  title = {Defending recommender systems: detection of profile injection attacks},
  journal = {Service Oriented Computing and Applications},
  year = {2007},
  volume = {1},
  pages = {157--170},
  number = {3},
  month = {November},
  abstract = {Abstract  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. Prior work
	has shown when profiles are reverse engineered to maximize influence;
	even a small number of malicious profiles can significantly bias
	the system. This paper describes a classification approach to the
	problem of detecting and responding to profile injection attacks.
	A number of attributes are identified that distinguish characteristics
	present in attack profiles in general, as well as an attribute generation
	approach for detecting profiles based on reverse engineered attack
	models. Three well-known classification algorithms are then used
	to demonstrate the combined benefit of these attributes and the impact
	the selection of classifier has with respect to improving the robustness
	of the recommender system. Our study demonstrates this technique
	significantly reduces the impact of the most powerful attack models
	previously studied, particularly when combined with a support vector
	machine classifier.},
  owner = {Chad Williams},
  timestamp = {2008.02.26},
  url = {http://dx.doi.org/10.1007/s11761-007-0013-0}
}

@INPROCEEDINGS{WMBSB06,
  author = {Chad Williams and Bamshad Mobasher and Robin Burke and Jeff Sandvig
	and Runa Bhaumik},
  title = {Detection of Obfuscated Attacks in Collaborative Recommender Systems},
  booktitle = {Proceedings of the ECAI’06 Workshop on Recommender Systems},
  year = {2006},
  address = {Held at the 17th European Conference on Artificial Intelligence (ECAI'06),
	Riva del Garda, Italy},
  month = {August},
  owner = {Chad Williams},
  timestamp = {2008.02.26}
}

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