Corpus ID: 34898011

Identifying Attack Models for Secure Recommendation

  title={Identifying Attack Models for Secure Recommendation},
  author={R. Burke and B. Mobasher and Roman Zabicki and Runa Bhaumik},
  • R. Burke, B. Mobasher, +1 author Runa Bhaumik
  • Published 2004
  • Computer Science
  • Publicly-accessible adaptive systems such as recommender systems present a security problem. Attackers, who cannot be readily distinguished from ordinary users, may introduce biased data in an attempt to force the system to "adapt" in a manner advantageous to them. Recent research has begun to examine the vulnerabilities of different recommendation techniques. In this paper, we outline some of the major issues in building secure recommender systems, concentrating in particular on the modeling… CONTINUE READING

    Figures and Topics from this paper.

    Explore Further: Topics Discussed in This Paper

    Effective Attack Models for Shilling Item-Based Collaborative Filtering Systems
    • 107
    • PDF
    Model-Based Collaborative Filtering as a Defense against Profile Injection Attacks
    • 131
    • PDF
    Shilling attacks against recommender systems: a comprehensive survey
    • 169
    • Highly Influenced
    • PDF


    Publications referenced by this paper.
    Evaluating collaborative filtering recommender systems
    • 5,135
    • PDF
    The Anatomy of a Large-Scale Hypertextual Web Search Engine
    • 14,407
    • PDF
    Hybrid Recommender Systems: Survey and Experiments
    • 3,485
    • PDF
    Shilling recommender systems for fun and profit
    • 586
    • Highly Influential
    • PDF
    Collaborative recommendation: A robustness analysis
    • 311
    • Highly Influential
    • PDF
    Knowledge-based recommender systems
    • 741
    • PDF
    NewsWeeder: Learning to Filter Netnews
    • 1,898
    An Intrusion-Detection Model
    • 1,292
    • PDF