A Contrast Metric for Fraud Detection in Rich Graphs
文献类型：期刊论文
| 作者 | Liu, Shenghua1; Hooi, Bryan2; Faloutsos, Christos2 |
| 刊名 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
 |
| 出版日期 | 2019-12-01 |
| 卷号 | 31期号:12页码:2235-2248 |
| 关键词 | Image edge detection Topology Measurement Robustness Time series analysis Tensile stress Manuals Graph mining time series fraud detection contrast suspiciousness |
| ISSN号 | 1041-4347 |
| DOI | 10.1109/TKDE.2018.2876531 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000498653800001 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/14922]  |
| 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 |
| 推荐引用方式 GB/T 7714 | 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. |
入库方式： OAI收割
来源：计算技术研究所
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