Classification features for attack detection in collaborative recommender systems
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Attacks and Remedies in Collaborative Recommendation
IEEE Intelligent Systems
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
Using data mining and recommender systems to scale up the requirements process
Proceedings of the 2nd international workshop on Ultra-large-scale software-intensive systems
Detecting profile injection attacks in collaborative filtering: a classification-based approach
WebKDD'06 Proceedings of the 8th Knowledge discovery on the web international conference on Advances in web mining and web usage analysis
Evaluating the dynamic properties of recommendation algorithms
Proceedings of the fourth ACM conference on Recommender systems
βP: A novel approach to filter out malicious rating profiles from recommender systems
Decision Support Systems
Defending imitating attacks in web credibility evaluation systems
Proceedings of the 22nd international conference on World Wide Web companion
| Hi-index | 0.00 |
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.