2,703 research outputs found
Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection
With the explosive growth of e-commerce, online transaction fraud has become
one of the biggest challenges for e-commerce platforms. The historical
behaviors of users provide rich information for digging into the users' fraud
risk. While considerable efforts have been made in this direction, a
long-standing challenge is how to effectively exploit internal user information
and provide explainable prediction results. In fact, the value variations of
same field from different events and the interactions of different fields
inside one event have proven to be strong indicators for fraudulent behaviors.
In this paper, we propose the Dual Importance-aware Factorization Machines
(DIFM), which exploits the internal field information among users' behavior
sequence from dual perspectives, i.e., field value variations and field
interactions simultaneously for fraud detection. The proposed model is deployed
in the risk management system of one of the world's largest e-commerce
platforms, which utilize it to provide real-time transaction fraud detection.
Experimental results on real industrial data from different regions in the
platform clearly demonstrate that our model achieves significant improvements
compared with various state-of-the-art baseline models. Moreover, the DIFM
could also give an insight into the explanation of the prediction results from
dual perspectives.Comment: 11 pages, 4 figure
PU GNN: Chargeback Fraud Detection in P2E MMORPGs via Graph Attention Networks with Imbalanced PU Labels
The recent advent of play-to-earn (P2E) systems in massively multiplayer
online role-playing games (MMORPGs) has made in-game goods interchangeable with
real-world values more than ever before. The goods in the P2E MMORPGs can be
directly exchanged with cryptocurrencies such as Bitcoin, Ethereum, or Klaytn
via blockchain networks. Unlike traditional in-game goods, once they had been
written to the blockchains, P2E goods cannot be restored by the game operation
teams even with chargeback fraud such as payment fraud, cancellation, or
refund. To tackle the problem, we propose a novel chargeback fraud prediction
method, PU GNN, which leverages graph attention networks with PU loss to
capture both the players' in-game behavior with P2E token transaction patterns.
With the adoption of modified GraphSMOTE, the proposed model handles the
imbalanced distribution of labels in chargeback fraud datasets. The conducted
experiments on three real-world P2E MMORPG datasets demonstrate that PU GNN
achieves superior performances over previously suggested methods.Comment: Under Review, Industry Trac
PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization
A multitude of toxic online behaviors, ranging from network attacks to
anonymous traffic and spam, have severely disrupted the smooth operation of
networks. Due to the inherent sender-receiver nature of network behaviors,
graph-based frameworks are commonly used for detecting anomalous behaviors.
However, in real-world scenarios, the boundary between normal and anomalous
behaviors tends to be ambiguous. The local heterophily of graphs interferes
with the detection, and existing methods based on nodes or edges introduce
unwanted noise into representation results, thereby impacting the effectiveness
of detection. To address these issues, we propose PhoGAD, a graph-based anomaly
detection framework. PhoGAD leverages persistent homology optimization to
clarify behavioral boundaries. Building upon this, the weights of adjacent
edges are designed to mitigate the effects of local heterophily. Subsequently,
to tackle the noise problem, we conduct a formal analysis and propose a
disentangled representation-based explicit embedding method, ultimately
achieving anomaly behavior detection. Experiments on intrusion, traffic, and
spam datasets verify that PhoGAD has surpassed the performance of
state-of-the-art (SOTA) frameworks in detection efficacy. Notably, PhoGAD
demonstrates robust detection even with diminished anomaly proportions,
highlighting its applicability to real-world scenarios. The analysis of
persistent homology demonstrates its effectiveness in capturing the topological
structure formed by normal edge features. Additionally, ablation experiments
validate the effectiveness of the innovative mechanisms integrated within
PhoGAD.Comment: Accepted by WSDM 202
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