Statistical attack detection

@inproceedings{Hurley2009StatisticalAD,
  title={Statistical attack detection},
  author={Neil J. Hurley and Zunping Cheng and Mi Zhang},
  booktitle={RecSys},
  year={2009}
}
It has been shown in recent years that effective profile injection or shilling attacks can be mounted on standard recommendation algorithms. These attacks consist of the insertion of bogus user profiles into the system database in order to manipulate the recommendation output, for example to promote or demote the predicted ratings for a particular product. A number of attack models have been proposed and some detection strategies to identify these attacks have been empirically evaluated. In… CONTINUE READING
Highly Cited
This paper has 93 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.
Showing 1-10 of 54 extracted citations

From similarity perspective: a robust collaborative filtering approach for service recommendations

Min Gao, Bin Ling, +3 authors Shun Li
Frontiers of Computer Science • 2017
View 10 Excerpts
Highly Influenced

Detecting abnormal profiles in collaborative filtering recommender systems

Journal of Intelligent Information Systems • 2016
View 4 Excerpts
Highly Influenced

Discovering shilling groups in a real e-commerce platform

Online Information Review • 2016
View 4 Excerpts
Highly Influenced

A comparative study of shilling attack detectors for recommender systems

2015 12th International Conference on Service Systems and Service Management (ICSSSM) • 2015
View 6 Excerpts
Highly Influenced

93 Citations

01020'11'13'15'17'19
Citations per Year
Semantic Scholar estimates that this publication has 93 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-2 of 2 references

Similar Papers

Loading similar papers…