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A Contrast Metric for Fraud Detection in Rich Graphs
Liu, Shenghua1; Hooi, Bryan2; Faloutsos, Christos2
2019-12-01
发表期刊IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN1041-4347
卷号31期号:12页码:2235-2248
摘要How can we detect fraud in a big graph with rich properties, as online fraudsters invest more resources, including purchasing large pools of fake user accounts and dedicated IPs, to hide their fraudulent attacks? To achieve robustness, existing approaches detected dense sub-graphs as suspicious patterns in an unsupervised way, such as average degree maximization. However, such approaches suffer from the bias of including more nodes than necessary, resulting in lower accuracy and increased need for manual verification. Therefore, we propose HoloScope, which introduces a novel metric "contrast suspiciousness" integrating information from graph topology and spikes to more accurately detect fraudulent users and objects. Contrast suspiciousness dynamically emphasizes the contrasting patterns between fraudsters and normal users, making HoloScope capable of distinguishing the synchronized and strange behaviors of fraudsters by means of topology, bursts and drops, and rating scores. In addition, we provide theoretical bounds for how much this method increases the time cost needed for fraudsters to conduct adversarial attacks. Moreover, HoloScope has a concise framework and sub-quadratic time complexity, making the algorithm reproducible and scalable. In extensive experiments, HoloScope achieved significant accuracy improvements on real data with injected labels and true labels, when compared with state-of-the-art fraud detection methods.
关键词Image edge detection Topology Measurement Robustness Time series analysis Tensile stress Manuals Graph mining time series fraud detection contrast suspiciousness
DOI10.1109/TKDE.2018.2876531
收录类别SCI
语种英语
资助项目Strategic Priority Research Program of CAS[XDA19020400] ; National 973 Program of China[2014CB340401] ; NSF of China[61772498] ; NSF of China[61872206] ; Beijing NSF[4172059] ; Army Research Laboratory[W911NF-09-2-0053] ; China Scholarship Council
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号WOS:000498653800001
出版者IEEE COMPUTER SOC
引用统计
被引频次:15[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://119.78.100.204/handle/2XEOYT63/14923
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Liu, Shenghua
作者单位1.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing 100864, Peoples R China
2.Carnegie Mellon Univ, Pittsburgh, PA 15213 USA
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Liu, Shenghua,Hooi, Bryan,Faloutsos, Christos. A Contrast Metric for Fraud Detection in Rich Graphs[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2019,31(12):2235-2248.
APA Liu, Shenghua,Hooi, Bryan,&Faloutsos, Christos.(2019).A Contrast Metric for Fraud Detection in Rich Graphs.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,31(12),2235-2248.
MLA Liu, Shenghua,et al."A Contrast Metric for Fraud Detection in Rich Graphs".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 31.12(2019):2235-2248.
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