The Python API to the ISC anomaly detection and classification framework. The framework implements Baysian statistical methods for anomaly detection and classification. Currently supported statistical models are: Poisson, Gamma and multivariate Gaussian distributions.
Questions regarding the use of the framework: https://groups.google.com/forum/#!forum/pyisc-users
Notice, pyISC/visISC has only been tested using 64 bit Python.
Install Python 2.7
Anaconda is the recommended Python distribution : https://www.continuum.io/downloads
Libraries:
- numpy, scipy, scikit-learn (required for running pyisc)
- matplotlib, ipython, jupyter, pandas (only required for running tutorial examples)
Install with anaconda:
(If you want to disable ssl verification when installing, you will find the instructions here.)
>> conda install numpy pandas scikit-learn ipython jupyter
If you intend to also install visISC, you have to downgrade the numpy installation to version 1.9
>> conda install numpy==1.9.3
Windows:
>> conda install mingw libpython==1.0
OS X:
Install the Xcode developer tools from App Store.
(search for suitable version with >> anaconda search -t conda swig)
Windows:
>> conda install --channel https://conda.anaconda.org/salilab swig
OS X:
>> conda install --channel https://conda.anaconda.org/minrk swig
For installing from source code, you need a git client
Then:
>> git clone https://github.com/STREAM3/pyisc --recursive
>> cd pyisc
>> python setup.py install
>> cd docs
>> jupyter notebook pyISC_tutorial.ipynb
If not opened automatically, click on pyISC_tutorial.ipynb in the web page that was opened in a web browser.
Emruli, B., Olsson, T., & Holst, A. (2017). pyISC: A Bayesian Anomaly Detection Framework for Python. In Florida Artificial Intelligence Research Society Conference. Retrieved from https://aaai.org/ocs/index.php/FLAIRS/FLAIRS17/paper/view/15527