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DPIF: A Framework for Distinguishing Unintentional Quality Problems From Potential Shilling Attacks

Mohan Li1, Yanbin Sun1, *, Shen Su1, Zhihong Tian1, Yuhang Wang1, *, Xianzhi Wang2

Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China.
School of Software, University of Technology Sydney, Sydney, Australia.

* Corresponding Authors: Yanbin Sun. Email: ;
   Yuhang Wang. Email: .

Computers, Materials & Continua 2019, 59(1), 331-344.


Maliciously manufactured user profiles are often generated in batch for shilling attacks. These profiles may bring in a lot of quality problems but not worthy to be repaired. Since repairing data always be expensive, we need to scrutinize the data and pick out the data that really deserves to be repaired. In this paper, we focus on how to distinguish the unintentional data quality problems from the batch generated fake users for shilling attacks. A two-steps framework named DPIF is proposed for the distinguishment. Based on the framework, the metrics of homology and suspicious degree are proposed. The homology can be used to represent both the similarities of text and the data quality problems contained by different profiles. The suspicious degree can be used to identify potential attacks. The experiments on real-life data verified that the proposed framework and the corresponding metrics are effective.


Cite This Article

M. Li, Y. Sun, S. Su, Z. Tian, Y. Wang et al., "Dpif: a framework for distinguishing unintentional quality problems from potential shilling attacks," Computers, Materials & Continua, vol. 59, no.1, pp. 331–344, 2019.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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