FraudFox: Adaptable Fraud Detection in the Real World
Gespeichert in:
| Veröffentlicht in: | MLHat (1. : 2020 : Online) Deployable machine learning for security defense |
|---|---|
| 1. Verfasser: | |
| Weitere Verfasser: | , |
| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
2020
|
| Schlagworte: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Tag hinzufügen | Titel | Jahr | Verfasser |
|---|---|---|
| MALOnt: An Ontology for Malware Threat Intelligence | 2020 | Rastogi, Nidhi |
| Toward Explainable and Adaptable Detection and Classification of Distributed Denial-of-Service Attacks | 2020 | Feng, Yebo |
| A Large-Scale Analysis of Attacker Activity in Compromised Enterprise Accounts | 2020 | Shah, Neil |
| DAPT 2020 - Constructing a Benchmark Dataset for Advanced Persistent Threats | 2020 | Myneni, Sowmya |
| FraudFox: Adaptable Fraud Detection in the Real World | 2020 | Butler, Matthew |
| Towards Practical Robustness Improvement for Object Detection in Safety-Critical Scenarios | 2020 | Hu, Zhisheng |
| Forecasting Network Intrusions from Security Logs Using LSTMs | 2020 | Mueller, W. Graham |
| Domain Generation Algorithm Detection Utilizing Model Hardening Through GAN-Generated Adversarial Examples | 2020 | Gould, Nathaniel |