28,813 research outputs found
Fraudulent User Detection Via Behavior Information Aggregation Network (BIAN) On Large-Scale Financial Social Network
Financial frauds cause billions of losses annually and yet it lacks efficient
approaches in detecting frauds considering user profile and their behaviors
simultaneously in social network . A social network forms a graph structure
whilst Graph neural networks (GNN), a promising research domain in Deep
Learning, can seamlessly process non-Euclidean graph data . In financial fraud
detection, the modus operandi of criminals can be identified by analyzing user
profile and their behaviors such as transaction, loaning etc. as well as their
social connectivity. Currently, most GNNs are incapable of selecting important
neighbors since the neighbors' edge attributes (i.e., behaviors) are ignored.
In this paper, we propose a novel behavior information aggregation network
(BIAN) to combine the user behaviors with other user features. Different from
its close "relatives" such as Graph Attention Networks (GAT) and Graph
Transformer Networks (GTN), it aggregates neighbors based on neighboring edge
attribute distribution, namely, user behaviors in financial social network. The
experimental results on a real-world large-scale financial social network
dataset, DGraph, show that BIAN obtains the 10.2% gain in AUROC comparing with
the State-Of-The-Art models.Comment: 6 pages, 1 figur
Understanding the Detection of View Fraud in Video Content Portals
While substantial effort has been devoted to understand fraudulent activity
in traditional online advertising (search and banner), more recent forms such
as video ads have received little attention. The understanding and
identification of fraudulent activity (i.e., fake views) in video ads for
advertisers, is complicated as they rely exclusively on the detection
mechanisms deployed by video hosting portals. In this context, the development
of independent tools able to monitor and audit the fidelity of these systems
are missing today and needed by both industry and regulators.
In this paper we present a first set of tools to serve this purpose. Using
our tools, we evaluate the performance of the audit systems of five major
online video portals. Our results reveal that YouTube's detection system
significantly outperforms all the others. Despite this, a systematic evaluation
indicates that it may still be susceptible to simple attacks. Furthermore, we
find that YouTube penalizes its videos' public and monetized view counters
differently, the former being more aggressive. This means that views identified
as fake and discounted from the public view counter are still monetized. We
speculate that even though YouTube's policy puts in lots of effort to
compensate users after an attack is discovered, this practice places the burden
of the risk on the advertisers, who pay to get their ads displayed.Comment: To appear in WWW 2016, Montr\'eal, Qu\'ebec, Canada. Please cite the
conference version of this pape
LIME: Low-Cost Incremental Learning for Dynamic Heterogeneous Information Networks
Understanding the interconnected relationships of large-scale information networks like social, scholar and Internet of Things networks is vital for tasks like recommendation and fraud detection. The vast majority of the real-world networks are inherently heterogeneous and dynamic, containing many different types of nodes and edges and can change drastically over time. The dynamicity and heterogeneity make it extremely challenging to reason about the network structure. Unfortunately, existing approaches are inadequate in modeling real-life networks as they require extensive computational resources and do not scale well to large, dynamically evolving networks. We introduce LIME, a better approach for modeling dynamic and heterogeneous information networks. LIME is designed to extract high-quality network representation with significantly lower memory resources and computational time over the state-of-the-art. Unlike prior work that uses a vector to encode each network node, we exploit the semantic relationships among network nodes to encode multiple nodes with similar semantics in shared vectors. We evaluate LIME by applying it to three representative network-based tasks, node classification, node clustering and anomaly detection, performing on three large-scale datasets. Our extensive experiments demonstrate that LIME not only reduces the memory footprint by over 80\% and computational time over 2x when learning network representation but also delivers comparable performance for downstream processing tasks
HitFraud: A Broad Learning Approach for Collective Fraud Detection in Heterogeneous Information Networks
On electronic game platforms, different payment transactions have different
levels of risk. Risk is generally higher for digital goods in e-commerce.
However, it differs based on product and its popularity, the offer type
(packaged game, virtual currency to a game or subscription service), storefront
and geography. Existing fraud policies and models make decisions independently
for each transaction based on transaction attributes, payment velocities, user
characteristics, and other relevant information. However, suspicious
transactions may still evade detection and hence we propose a broad learning
approach leveraging a graph based perspective to uncover relationships among
suspicious transactions, i.e., inter-transaction dependency. Our focus is to
detect suspicious transactions by capturing common fraudulent behaviors that
would not be considered suspicious when being considered in isolation. In this
paper, we present HitFraud that leverages heterogeneous information networks
for collective fraud detection by exploring correlated and fast evolving
fraudulent behaviors. First, a heterogeneous information network is designed to
link entities of interest in the transaction database via different semantics.
Then, graph based features are efficiently discovered from the network
exploiting the concept of meta-paths, and decisions on frauds are made
collectively on test instances. Experiments on real-world payment transaction
data from Electronic Arts demonstrate that the prediction performance is
effectively boosted by HitFraud with fast convergence where the computation of
meta-path based features is largely optimized. Notably, recall can be improved
up to 7.93% and F-score 4.62% compared to baselines.Comment: ICDM 201
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