Detecting fake reviewers in heterogeneous networks of buyers and sellers: a collaborative training-based spammer group algorithm

Cybersecurity - Tập 6 Số 1
Qi Zhang1, Zhenlin Liang2, Shujuan Ji1, Baocai Xing3, Dickson K.W. Chiu4
1Shandong Provincial Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao, China
2Zhejiang University-University of Illinois at Urbana-Champaign Institute, Haining, China
3School of Civil Engineering, Beijing Jiaotong University, Beijing, China
4Faculty of Education, The University of Hong Kong, Pok Fu Lam, China

Tóm tắt

AbstractIt is not uncommon for malicious sellers to collude with fake reviewers (also called spammers) to write fake reviews for multiple products to either demote competitors or promote their products’ reputations, forming a gray industry chain. To detect spammer groups in a heterogeneous network with rich semantic information from both buyers and sellers, researchers have conducted extensive research using Frequent Item Mining-based and graph-based methods. However, these methods cannot detect spammer groups with cross-product attacks and do not jointly consider structural and attribute features, and structure-attribute correlation, resulting in poorer detection performance. Therefore, we propose a collaborative training-based spammer group detection algorithm by constructing a heterogeneous induced sub-network based on the target product set to detect cross-product attack spammer groups. To jointly consider all available features, we use the collaborative training method to learn the feature representations of nodes. In addition, we use the DBSCAN clustering method to generate candidate groups, exclude innocent ones, and rank them to obtain spammer groups. The experimental results on real-world datasets indicate that the overall detection performance of the proposed method is better than that of the baseline methods.

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Cybersecurity

Tập 6 Số 1

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