A5102796888- Imbalanced Data Classification Techniques
- Spam and Phishing Detection
- Functional Brain Connectivity Studies
- Advanced Malware Detection Techniques
- Advanced Neuroimaging Techniques and Applications
- Topic Modeling
- Neuroscience and Neuropharmacology Research
Chinese Academy of Sciences
2016-2022
Institute of Computing Technology
2022
Wuhan Institute of Physics and Mathematics
2016
Anti-fraud machine learning systems are perpetually confronted with the significant challenge of concept drift, driven by continuous and intense evolution fraudulent techniques. That is, outdated models trained on historical behaviors often fall short in addressing evolving tactics malicious users over time. The key issue lies effectively tackling rapid fraudsters' to detect these emerging unforeseen anomalies. In this paper, we propose a solution directly accessing real-time data...
The outbreak of COVID-19 burgeons newborn services on online platforms and simultaneously buoys multifarious fraud activities. Due to the rapid technological commercial innovation that opens up an ever-expanding set products, insufficient labeling data renders existing supervised or semi-supervised detection models ineffective in these emerging services. However, ever accumulated user behavioral might be helpful improving performance To this end, paper, we propose pre-train behavior...