Morpheus Models
Morpheus comes with a number of pre-trained models with corresponding training, validation scripts, and datasets. The latest release of these models can be found here.
Model |
GPU Mem Req |
Description |
|---|---|---|
| Anomalous Behavior Profiling (ABP) | 2015MiB | This model is an example of a binary classifier to differentiate between anomalous GPU behavior such as crypto mining / GPU malware, and non-anomalous GPU-based workflows (for example, ML/DL training). The model is an XGBoost model. |
| Digital Fingerprinting (DFP) | 4.97MiB | This use case is currently implemented to detect changes in a users’ behavior that indicates a change from a human to a machine or a machine to a human. The model is an ensemble of an Autoencoder and fast Fourier transform reconstruction. |
| Fraud Detection | 76.55MiB | This model shows an application of a graph neural network for fraud detection in a credit card transaction graph. A transaction dataset that includes three types of nodes, transaction, client, and merchant nodes is used for modeling. A combination of GraphSAGE along with XGBoost is used to identify frauds in the transaction networks. |
| Ransomware Detection Model | n/a | This model shows an application of DOCA AppShield to use data from volatile memory to classify processes as ransomware or bengin. This model uses a sliding window over time and feeds derived data into a random forest classifiers of various lengths depending on the amount of data collected. |
| Flexible Log Parsing | 1612MiB | This model is an example of using Named Entity Recognition (NER) for log parsing, specifically Apache HTTP Server logs. |