PyMISP
cuml
PyMISP | cuml | |
---|---|---|
3 | 10 | |
422 | 3,903 | |
1.7% | 1.1% | |
9.2 | 9.3 | |
2 days ago | 6 days ago | |
Python | C++ | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
PyMISP
- FLaNK Stack Weekly for 13 November 2023
-
Get CrowdSec IOCs feed into MISP
You might consider misp feed https://github.com/MISP/PyMISP/tree/main/examples/feed-generator, basically itβs the best way to collect IOCs and import them into a MISP instance. These feeds help to correlate IOCs without manually launching the MISP module every time for each IOC, this also reduce the workload on your API servers as the list is cached locally on the MISP and updated every day.
-
Ingesting IOCs in to CS from MISP
If you're in Python, you can use PyMISP to login and get the new indicators, and then FalconPy to import them into your CrowdStrike tenant. (Basically the reverse of what the MISP-tools example is doing. You could start here and alter the logic.)
cuml
- FLaNK Stack Weekly for 13 November 2023
-
Is it possible to run Sklearn models on a GPU?
sklearn can't, bit take a look at cuML (https://github.com/rapidsai/cuml ). It uses the same API as sklearn but executes on GPU.
-
[P] Looking for state of the art clustering algorithms
As a companion to the other comments, I'd like to mention that the RAPIDS library cuML provides GPU-accelerated versions of quite a few of the algorithms mentioned in this thread (HDBSCAN, UMAP, SVM, PCA, {Exact, Approximate} Nearest Neighbors, DBSCAN, KMeans, etc.).
-
Is there a multi regression model that works on GPU?
CuML
- [D] What's your favorite unpopular/forgotten Machine Learning method?
- Machine Learning with PyTorch and Scikit-Learn β The *New* Python ML Book
-
What are the advantages and disadvantages of using GPU for machine learning/ deep learning/ scientific computation over the conventional CPU software acceleration?
Did they implement the clustering algorithm themselves? cuML is a GPU-accelerated scikit-learn-like package that covers many of the common ML algorithms.
-
Intel Extension for Scikit-Learn
https://github.com/rapidsai/cuml
> cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn. For large datasets, these GPU-based implementations can complete 10-50x faster than their CPU equivalents. For details on performance, see the cuML Benchmarks Notebook.
-
GPU Based Kernel-PCA
Cython code
-
Python Machine Learning Guy getting started with CUDA. What should I be brushing up on?
Take a look at RAPIDS CUML https://github.com/rapidsai/cuml. It's useful for most common ML algorithms. Feel free to create Github issues for feature requests & bugs.
What are some alternatives?
MISP-QRadar-Integration - The Project can be used to integrate QRadar with MISP Threat Sharing Platform
scikit-learn - scikit-learn: machine learning in Python
yeti - Your Everyday Threat Intelligence
scikit-learn-intelex - Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
vimGPT - Browse the web with GPT-4V and Vimium
scikit-cuda - Python interface to GPU-powered libraries
clipea - ππ’ Like Clippy but for the CLI. A blazing fast AI helper for your command line
hummingbird - Hummingbird compiles trained ML models into tensor computation for faster inference.
MISP-tools - Import CrowdStrike Threat Intelligence into your instance of MISP
cudf - cuDF - GPU DataFrame Library
PaK-Stocks - Stocks
evojax