[go: up one dir, main page]

Liu et al., 2020 - Google Patents

Alleviating the inconsistency problem of applying graph neural network to fraud detection

Liu et al., 2020

View PDF
Document ID
14930576432880236829
Author
Liu Z
Dou Y
Yu P
Deng Y
Peng H
Publication year
Publication venue
Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval

External Links

Snippet

Graph-based models have been widely used to fraud detection tasks. Owing to the development of Graph Neural Networks~(GNNs), recent works have proposed many GNN- based fraud detectors based on either homogeneous or heterogeneous graphs. These …
Continue reading at dl.acm.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/55—Detecting local intrusion or implementing counter-measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
    • G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce, e.g. shopping or e-commerce
    • G06Q30/01—Customer relationship, e.g. warranty
    • G06Q30/018—Business or product certification or verification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/01—Social networking
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic

Similar Documents

Publication Publication Date Title
Liu et al. Alleviating the inconsistency problem of applying graph neural network to fraud detection
Dou et al. Enhancing graph neural network-based fraud detectors against camouflaged fraudsters
Dhieb et al. A secure ai-driven architecture for automated insurance systems: Fraud detection and risk measurement
Lorenz et al. Machine learning methods to detect money laundering in the bitcoin blockchain in the presence of label scarcity
Zhao et al. Fedinv: Byzantine-robust federated learning by inversing local model updates
Li et al. Identifying illicit addresses in bitcoin network
Savage et al. Detection of money laundering groups using supervised learning in networks
Chen et al. Bitcoin theft detection based on supervised machine learning algorithms
Nguyen et al. Manipulating recommender systems: A survey of poisoning attacks and countermeasures
Badawi et al. Detection of money laundering in bitcoin transactions
Lee et al. Machine learning based bitcoin address classification
Saxena et al. Classifying transactional addresses using supervised learning approaches over Ethereum blockchain
Kang et al. Graph anomaly detection with disentangled prototypical autoencoder for phishing scam detection in cryptocurrency transactions
Tian et al. Attention-based graph neural network for identifying illicit bitcoin addresses
Sharma et al. Graph neural network-based anomaly detection in blockchain network
Liu et al. A survey on blockchain abnormal transaction detection
Mahalaxmi et al. Data Analysis with Blockchain Technology: A Review
Li et al. Contrastive learning for money laundering detection: Node-subgraph-node method with context aggregation and enhancement strategy
Ampel et al. Disrupting ransomware actors on the bitcoin blockchain: A graph embedding approach
Al-Hashedi et al. A conceptual model to identify illegal activities on the bitcoin system
Xu et al. Crowdsourcing Fraud Detection Over Heterogeneous Temporal MMMA Graph
Saxena et al. Integration of back-propagation neural network to classify of cybercriminal entities in blockchain
Sherly et al. A improved incremental and interactive frequent pattern mining techniques for market basket analysis and fraud detection in distributed and parallel systems
Al-Hashedi et al. A supervised model to detect suspicious activities in the bitcoin network
Zhang et al. A bitcoin address multi-classification mechanism based on bipartite graph-based maximization consensus