A Customer Level Fraudulent Activity Detection Benchmark for Enhancing Machine Learning Model Research and Evaluation
Fuente:
arXiv
Guardado en:
| Autores principales: | Jing, Phoebe, Gao, Yijing, Zeng, Xianlong |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
RiskSEA : A Scalable Graph Embedding for Detecting On-chain Fraudulent Activities on the Ethereum Blockchain
por: Agarwal, Ayush, et al.
Publicado: (2024)
por: Agarwal, Ayush, et al.
Publicado: (2024)
Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017
por: Xu, Zhaoyang, et al.
Publicado: (2025)
por: Xu, Zhaoyang, et al.
Publicado: (2025)
Research on Dynamic Data Flow Anomaly Detection based on Machine Learning
por: Wang, Liyang, et al.
Publicado: (2024)
por: Wang, Liyang, et al.
Publicado: (2024)
Machine Learning Transferability for Malware Detection
por: Vieira, César, et al.
Publicado: (2026)
por: Vieira, César, et al.
Publicado: (2026)
A Cryptographic Perspective on Mitigation vs. Detection in Machine Learning
por: Gluch, Greg, et al.
Publicado: (2025)
por: Gluch, Greg, et al.
Publicado: (2025)
On The Fragility of Benchmark Contamination Detection in Reasoning Models
por: Wang, Han, et al.
Publicado: (2025)
por: Wang, Han, et al.
Publicado: (2025)
Model-based Large Language Model Customization as Service
por: Wu, Zhaomin, et al.
Publicado: (2024)
por: Wu, Zhaomin, et al.
Publicado: (2024)
Feature Selection via GANs (GANFS): Enhancing Machine Learning Models for DDoS Mitigation
por: Patel, Harsh
Publicado: (2025)
por: Patel, Harsh
Publicado: (2025)
C2A: Client-Customized Adaptation for Parameter-Efficient Federated Learning
por: Kim, Yeachan, et al.
Publicado: (2024)
por: Kim, Yeachan, et al.
Publicado: (2024)
A Comprehensive Study of Supervised Machine Learning Models for Zero-Day Attack Detection: Analyzing Performance on Imbalanced Data
por: Lotfi, Zahra, et al.
Publicado: (2025)
por: Lotfi, Zahra, et al.
Publicado: (2025)
Impacts of Data Preprocessing and Hyperparameter Optimization on the Performance of Machine Learning Models Applied to Intrusion Detection Systems
por: Lima, Mateus Guimarães, et al.
Publicado: (2024)
por: Lima, Mateus Guimarães, et al.
Publicado: (2024)
Statement-Level Vulnerability Detection: Learning Vulnerability Patterns Through Information Theory and Contrastive Learning
por: Nguyen, Van, et al.
Publicado: (2022)
por: Nguyen, Van, et al.
Publicado: (2022)
Locking Machine Learning Models into Hardware
por: Clifford, Eleanor, et al.
Publicado: (2024)
por: Clifford, Eleanor, et al.
Publicado: (2024)
FedCAP: Robust Federated Learning via Customized Aggregation and Personalization
por: Li, Youpeng, et al.
Publicado: (2024)
por: Li, Youpeng, et al.
Publicado: (2024)
Differentially Private Preference Data Synthesis for Large Language Model Alignment
por: Gao, Fengyu, et al.
Publicado: (2026)
por: Gao, Fengyu, et al.
Publicado: (2026)
A Study on the Importance of Features in Detecting Advanced Persistent Threats Using Machine Learning
por: Hallaji, Ehsan, et al.
Publicado: (2025)
por: Hallaji, Ehsan, et al.
Publicado: (2025)
RMF: A Risk Measurement Framework for Machine Learning Models
por: Schröder, Jan, et al.
Publicado: (2024)
por: Schröder, Jan, et al.
Publicado: (2024)
Scalable Federated Unlearning via Isolated and Coded Sharding
por: Lin, Yijing, et al.
Publicado: (2024)
por: Lin, Yijing, et al.
Publicado: (2024)
Beyond Detection: A Comprehensive Benchmark and Study on Representation Learning for Fine-Grained Webshell Family Classification
por: Han, Feijiang
Publicado: (2025)
por: Han, Feijiang
Publicado: (2025)
Analyzing Inference Privacy Risks Through Gradients in Machine Learning
por: Li, Zhuohang, et al.
Publicado: (2024)
por: Li, Zhuohang, et al.
Publicado: (2024)
An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey
por: Ige, Tosin, et al.
Publicado: (2024)
por: Ige, Tosin, et al.
Publicado: (2024)
A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges
por: Tripathy, Sudhanshu Sekhar, et al.
Publicado: (2025)
por: Tripathy, Sudhanshu Sekhar, et al.
Publicado: (2025)
Android Malware Detection: A Machine Leaning Approach
por: Abdulla, Hasan
Publicado: (2025)
por: Abdulla, Hasan
Publicado: (2025)
NoPhish: Efficient Chrome Extension for Phishing Detection Using Machine Learning Techniques
por: Thaqi, Leand, et al.
Publicado: (2024)
por: Thaqi, Leand, et al.
Publicado: (2024)
Translating Expert Intuition into Quantifiable Features: Encode Investigator Domain Knowledge via LLM for Enhanced Predictive Analytics
por: Jing, Phoebe, et al.
Publicado: (2024)
por: Jing, Phoebe, et al.
Publicado: (2024)
Exploring Robust Intrusion Detection: A Benchmark Study of Feature Transferability in IoT Botnet Attack Detection
por: Guerra-Manzanares, Alejandro, et al.
Publicado: (2026)
por: Guerra-Manzanares, Alejandro, et al.
Publicado: (2026)
WAREX: Web Agent Reliability Evaluation on Existing Benchmarks
por: Kara, Su, et al.
Publicado: (2025)
por: Kara, Su, et al.
Publicado: (2025)
Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage
por: Rashid, Md Rafi Ur, et al.
Publicado: (2024)
por: Rashid, Md Rafi Ur, et al.
Publicado: (2024)
A Backdoor-based Explainable AI Benchmark for High Fidelity Evaluation of Attributions
por: Yang, Peiyu, et al.
Publicado: (2024)
por: Yang, Peiyu, et al.
Publicado: (2024)
Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability
por: Kuikel, Shova, et al.
Publicado: (2025)
por: Kuikel, Shova, et al.
Publicado: (2025)
LogGuardQ: A Cognitive-Enhanced Reinforcement Learning Framework for Cybersecurity Anomaly Detection in Security Logs
por: de Sousa, Umberto Gonçalves
Publicado: (2025)
por: de Sousa, Umberto Gonçalves
Publicado: (2025)
A Novel Ensemble Learning Approach for Enhanced IoT Attack Detection: Redefining Security Paradigms in Connected Systems
por: Abdeljaber, Hikmat A. M., et al.
Publicado: (2025)
por: Abdeljaber, Hikmat A. M., et al.
Publicado: (2025)
ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning
por: Xu, Zhangchen, et al.
Publicado: (2024)
por: Xu, Zhangchen, et al.
Publicado: (2024)
Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning
por: Gao, Fengyu, et al.
Publicado: (2024)
por: Gao, Fengyu, et al.
Publicado: (2024)
Trusted Machine Learning Models Unlock Private Inference for Problems Currently Infeasible with Cryptography
por: Shumailov, Ilia, et al.
Publicado: (2025)
por: Shumailov, Ilia, et al.
Publicado: (2025)
Decentralized Weather Forecasting via Distributed Machine Learning and Blockchain-Based Model Validation
por: Umar, Rilwan, et al.
Publicado: (2025)
por: Umar, Rilwan, et al.
Publicado: (2025)
Semi-Supervised Learning for Anomaly Traffic Detection via Bidirectional Normalizing Flows
por: Dang, Zhangxuan, et al.
Publicado: (2024)
por: Dang, Zhangxuan, et al.
Publicado: (2024)
GPML: Graph Processing for Machine Learning
por: Jaber, Majed, et al.
Publicado: (2025)
por: Jaber, Majed, et al.
Publicado: (2025)
The Data Minimization Principle in Machine Learning
por: Ganesh, Prakhar, et al.
Publicado: (2024)
por: Ganesh, Prakhar, et al.
Publicado: (2024)
Adversarial Machine Learning Threats to Spacecraft
por: Thummala, Rajiv, et al.
Publicado: (2024)
por: Thummala, Rajiv, et al.
Publicado: (2024)
Ejemplares similares
-
RiskSEA : A Scalable Graph Embedding for Detecting On-chain Fraudulent Activities on the Ethereum Blockchain
por: Agarwal, Ayush, et al.
Publicado: (2024) -
Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017
por: Xu, Zhaoyang, et al.
Publicado: (2025) -
Research on Dynamic Data Flow Anomaly Detection based on Machine Learning
por: Wang, Liyang, et al.
Publicado: (2024) -
Machine Learning Transferability for Malware Detection
por: Vieira, César, et al.
Publicado: (2026) -
A Cryptographic Perspective on Mitigation vs. Detection in Machine Learning
por: Gluch, Greg, et al.
Publicado: (2025)