Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives
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Summary
This work describes how attacks against fraud detection systems differ from other applications of adversarial machine learning, and proposes a number of interesting directions to bridge this gap.
- Type
- preprint
- Published
- 2023-06-18
- Cited by
- 30
- References
- 50
- Access
- Open access
- OpenAlex
- https://openalex.org/W4383468834
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:259341933
Keywords
Adversarial system, Adversarial machine learning, Computer science, Machine learning, Artificial intelligence
References
- Adaptive Fraud Detection
- Credit card fraud detection and concept-drift adaptation with delayed supervised information
- Intriguing properties of neural networks
- A hybrid model for plastic card fraud detection systems
- BankSealer: A decision support system for online banking fraud analysis and investigation
- Just-In-Time Classifiers for Recurrent Concepts
- Adversarial machine learning
- Mimicry attacks on host-based intrusion detection systems
- Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
- The Limitations of Deep Learning in Adversarial Settings
- Feature engineering strategies for credit card fraud detection
- DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
- Adversarial learning
- Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
- Towards Evaluating the Robustness of Neural Networks
- Practical Black-Box Attacks against Machine Learning
- ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models
- SCARFF: A scalable framework for streaming credit card fraud detection with spark
- Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy
- Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients
Cited by
- Regulation of Fraud in Civil Code: a Comparative Study Between The Indonesian Civil Code and Netherlands Nieuw Burgerlijk Wetboek
- Assessing adversarial attacks in real-world fraud detection
- Machine Learning Methods for Credit Card Fraud Detection: A Survey
- An Analytical Framework for Evaluating Successful Poisoning Attacks on Machine Learning Algorithms
- The Evolving Landscape of Network Threats: Classification, Defense Challenges, and Future Directions
- Foe for Fraud: Transferable Adversarial Attacks in Credit Card Fraud Detection
- Next-generation Fraud Detection in U.S Financial Systems: Evaluating Hybrid AI and Rule-based Models for Real-time Threat Mitigation
- TabAttackBench: A Benchmark for Adversarial Attacks on Tabular Data
- Hypergraph-based contrastive learning for enhanced fraud detection
- Beyond the veil: unpacking money laundering through economic models, emerging technologies, and government capture
- A survey of adversarial attacks on machine learning
- FUSE: Frequency Spatial Ensemble Attack with Dual Source Attention
- Adversarial Artificial Intelligence (AI) in Cybercrime Detection and Forensics
- Adaptive Real-Time Financial Fraud Detection with Explainable AI Tools
- Real-Time Fraud Detection in Digital Banking using Explainable Machine Learning and Data Engineering Pipeline
- UniFi-LLM: A Unified Large Language Model for Financial Data Generation and Fraud Prediction
- Fraud-RLA: A Reinforcement Learning Adversarial Attack Against Credit Card Fraud Detection
- Beyond vulnerabilities: A comprehensive survey of adversarial attacks across domains
- FinFraud-LLM: Exploring Large Language Models for Financial Fraud Detection
- Mitigating Metamorphic Malware Through Adversarial Learning Techniques
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