1,964 research outputs found
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
SNAPSKETCH: Graph Representation Approach for Anomaly Detection in Graph Stream
A novel unsupervised graph representation approach in a graph stream called SNAPSKETCH for anomaly detection is proposed. It first performs a fixed-length random walk from each node in a network and constructs n-shingles from a walk path. The top discriminative n-shingles identified using a frequency measure are projected into a dimensional projection vector chosen uniformly at random. Finally, a network is sketched into a low-dimensional sketch vector using a simplified hashing of projection vector and the cost of shingles. Using the learned sketch vector, anomaly detection is done using the state-of-the-art anomaly detection approach called RRCF [1]. SNAPSKETCHhas several advantages: Fully unsupervised learning, Constant memory space usage, Entire-graph embedding, and Real-time anomaly detection
MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams
Given a stream of graph edges from a dynamic graph, how can we assign anomaly
scores to edges in an online manner, for the purpose of detecting unusual
behavior, using constant time and memory? Existing approaches aim to detect
individually surprising edges. In this work, we propose MIDAS, which focuses on
detecting microcluster anomalies, or suddenly arriving groups of suspiciously
similar edges, such as lockstep behavior, including denial of service attacks
in network traffic data. MIDAS has the following properties: (a) it detects
microcluster anomalies while providing theoretical guarantees about its false
positive probability; (b) it is online, thus processing each edge in constant
time and constant memory, and also processes the data 162-644 times faster than
state-of-the-art approaches; (c) it provides 42%-48% higher accuracy (in terms
of AUC) than state-of-the-art approaches.Comment: 8 pages, Accepted at AAAI Conference on Artificial Intelligence
(AAAI), 2020 [oral paper]; minor fixes, updated experiment
AIOps for a Cloud Object Storage Service
With the growing reliance on the ubiquitous availability of IT systems and
services, these systems become more global, scaled, and complex to operate. To
maintain business viability, IT service providers must put in place reliable
and cost efficient operations support. Artificial Intelligence for IT
Operations (AIOps) is a promising technology for alleviating operational
complexity of IT systems and services. AIOps platforms utilize big data,
machine learning and other advanced analytics technologies to enhance IT
operations with proactive actionable dynamic insight.
In this paper we share our experience applying the AIOps approach to a
production cloud object storage service to get actionable insights into
system's behavior and health. We describe a real-life production cloud scale
service and its operational data, present the AIOps platform we have created,
and show how it has helped us resolving operational pain points.Comment: 5 page
Deep neural networks in the cloud: Review, applications, challenges and research directions
Deep neural networks (DNNs) are currently being deployed as machine learning technology in a wide
range of important real-world applications. DNNs consist of a huge number of parameters that require
millions of floating-point operations (FLOPs) to be executed both in learning and prediction modes. A
more effective method is to implement DNNs in a cloud computing system equipped with centralized
servers and data storage sub-systems with high-speed and high-performance computing capabilities.
This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing.
Various DNN complexities associated with different architectures are presented and discussed alongside
the necessities of using cloud computing. We also present an extensive overview of different cloud
computing platforms for the deployment of DNNs and discuss them in detail. Moreover, DNN applications
already deployed in cloud computing systems are reviewed to demonstrate the advantages of using
cloud computing for DNNs. The paper emphasizes the challenges of deploying DNNs in cloud computing
systems and provides guidance on enhancing current and new deployments.The EGIA project (KK-2022/00119The
Consolidated Research Group MATHMODE (IT1456-22
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