Turbo: Fraud Detection in Deposit-free Leasing Service via Real-Time Behavior Network Mining

Hu, Sihao; Zhang, Xuhong; Zhou, Junfeng; Ji, Shouling; Yuan, Jiaqi; Li, Zhao; Wang, Zhipeng; Chen, Qi; He, Qinming; Fang, Liming

doi:10.1109/icde51399.2021.00289

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…Among GNNs, GraphSAGE outperforms both GCN and GAT by a large margin, which can be attributed to the fact that homogeneous GNNs can introduce a large amount of noise in the multi-hop aggregation, which de-anonymization is susceptible to, especially given that Ethereum account nodes that are highly likely to be linked to popular account nodes. GraphSAGE is more resistant to noise [14] because of its skip-connection mechanism. Notably, the basic BERT4ETH can exactly de-anonymize 16.32% of account pairs, offering a significant improvement of 9.7 AP gain on HR@1.…”

Section: Methodsmentioning

confidence: 99%

BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection

Zhang

Bingqiao

et al. 2023

Proceedings of the ACM Web Conference 2023

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As various forms of fraud proliferate on Ethereum, it is imperative to safeguard against these malicious activities to protect susceptible users from being victimized. While current studies solely rely on graph-based fraud detection approaches, it is argued that they may not be well-suited for dealing with highly repetitive, skew-distributed and heterogeneous Ethereum transactions. To address these challenges, we propose BERT4ETH, a universal pretrained Transformer encoder that serves as an account representation extractor for detecting various fraud behaviors on Ethereum. BERT4ETH features the superior modeling capability of Transformer to capture the dynamic sequential patterns inherent in Ethereum transactions, and addresses the challenges of pre-training a BERT model for Ethereum with three practical and effective strategies, namely repetitiveness reduction, skew alleviation and heterogeneity modeling. Our empirical evaluation demonstrates that BERT4ETH outperforms state-of-the-art methods with significant enhancements in terms of the phishing account detection and de-anonymization tasks. The code for BERT4ETH is available at: https://github.com/git-disl/BERT4ETH.

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Section: Methodsmentioning

confidence: 99%

BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection

Zhang

Bingqiao

et al. 2023

Proceedings of the ACM Web Conference 2023

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“…Graph has been a very popular structure to represent the ubiquitous relationships between entities in e-commerce [8,13]. In GIFT system, we construct a heterogeneous graph consisting of two types of nodes and multiple types of edges to guide the subsequent feature transfer process.…”

Section: Graph Constructionmentioning

confidence: 99%

GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction

Hu¹,

Cao²,

Gong³

et al. 2022

Preprint

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Short video has witnessed rapid growth in China and shows a promising market for promoting the sales of products in e-commerce platforms like Taobao. To ensure the freshness of the content, the platform needs to release a large number of new videos every day, which makes the conventional click-through rate (CTR) prediction model suffer from the severe item cold-start problem.In this paper, we propose GIFT, an efficient Graph-guIded Feature Transfer system, to fully take advantages of the rich information of warmed-up videos that related to the cold-start video. More specifically, we conduct feature transfer from warmed-up videos to those cold-start ones by involving the physical and semantic linkages into a heterogeneous graph. The former linkages consist of those explicit relationships (e.g., sharing the same category, under the same authorship etc.), while the latter measure the proximity of multimodal representations of two videos. In practice, the style, content, and even the recommendation pattern are pretty similar among those physically or semantically related videos. Besides, in order to provide the robust id representations and historical statistics obtained from warmed-up neighbors that cold-start videos covet most, we elaborately design the transfer function to make aware of different transferred features from different types of nodes and edges along the metapath on the graph. Extensive experiments on a large real-world dataset show that our GIFT system outperforms SOTA methods significantly and brings a 6.82% lift on click-through rate (CTR) in the homepage of Taobao App.

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“…Although the whole process lasted only five minutes, a staggering wealth of around 27 million dollars was transferred between the participants. 1 https://github.com/Bayi-Hu/Pump-and-Dump-Detection-on-Cryptocurrency As P&Ds occur frequently and attract more public attention, it becomes imperative to detect such fraudulent activities [13,27] and inform potentially susceptible investors before they become victims. Existing efforts [14][15][16]20] mainly fall into the category of P&D post-detection, which aims to detect whether a P&D has happened or is happening.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1: The $NAS pump-and-dump coordinated by the Telegram channel "Binance Trading Signals".As P&Ds occur frequently and attract more public attention, it becomes imperative to detect such fraudulent activities[13,27] and inform potentially susceptible investors before they become victims. Existing efforts[14][15][16]20] mainly fall into the category of P&D post-detection, which aims to detect whether a P&D has happened or is happening.…”

mentioning

confidence: 99%

Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump

Hu¹,

Zhang²,

Lu³

et al. 2022

Preprint

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As the pump-and-dump schemes (P&Ds) proliferate in the cryptocurrency market, it becomes imperative to detect such fraudulent activities in advance, to inform potentially susceptible investors before they become victims.In this paper, we focus on the target coin prediction task, i.e., to predict the pump probability of all coins listed in the target exchange before a pump. We conduct a comprehensive study of the latest P&Ds, investigate 709 events organized in Telegram channels from Jan. 2019 to Jan. 2022, and unearth some abnormal yet interesting patterns of P&Ds. Empirical analysis demonstrates that pumped coins exhibit intra-channel homogeneity and inter-channel heterogeneity, which inspires us to develop a novel sequence-based neural network named SNN. Specifically, SNN encodes each channel's pump history as a sequence representation via a positional attention mechanism, which filters useful information and alleviates the noise introduced when the sequence length is long. We also identify and address the coin-side cold-start problem in a practical setting. Extensive experiments show a lift of 1.6% AUC and 41.0% Hit Ratio@3 brought by our method, making it well-suited for real-world application. As a side contribution, we release the source code of our entire data science pipeline on GitHub 1 , along with the dataset tailored for studying the latest P&Ds.

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Turbo: Fraud Detection in Deposit-free Leasing Service via Real-Time Behavior Network Mining

Cited by 6 publications

References 15 publications

BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection

BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection

GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction

Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump

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