=============
AntiNex 1.0.0
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.. figure:: /_images/image_training-ai-with-antinex.png
   :width: 600pt

Date: 2018-03-30

I am looking forward to seeing what we can teach Deep Neural Networks about defending software from network exploits. I recently got pretty hooked into `debugging malware`_, and that led me to building datasets for teaching DNN's to predict an attack vs a non-attack record within the OSI network layers.

The first non-scaler datasets results were not very great and had a wide variance between 66-89% predictive accuracy. 

Well as I kept trying different DNN configurations and datasets (and wondering why my xgboost scores were always better), I found out I needed to bound my data in a known range using an `sklearn.preprocessing.MinMaxScaler`_. Wow... I was pretty shocked at how fast the accuracies shot up from there. With just a wide, two-layer DNN the same Keras and Tensorflow models are now repeatedly predicting attacks vs non-attack network traffic with over **99.7%** accuracy. In fact, all of the AntiNex datasets including: Django, Flask, React + Redux, Vue, and Spring can be used to train a DNN within AntiNex to predict an attack with over 99.7% accuracy. I decided this was good enough to build a data pipeline around and made it auto-scale the data before training and making live predictions. By default, AntiNex now automatically converts datasets using this scaler normalization to bound all data into a range between [-1.0, 1.0]. This appears to not only work on classification problems, but for `predicting stock prices`_ using regression as well.

.. _debugging malware: https://github.com/jay-johnson/nerfball
.. _sklearn.preprocessing.MinMaxScaler: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html
.. _predicting stock prices: https://github.com/jay-johnson/train-ai-with-django-swagger-jwt/blob/56ed94e6807b0f3b0dd7938187d3d97109fd0cdb/tests/scaler-regression.json#L4-L11

I have put the technical docs together on how AntiNex works:

http://antinex.readthedocs.io/en/latest/

Here are two Jupyter notebooks for reviewing the process, analysis and validation of the scores:

Standalone Jupyter Notebook without any AntiNex components 
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https://github.com/jay-johnson/antinex-core/blob/master/docker/notebooks/AntiNex-Protecting-Django.ipynb

Using Pre-trained Deep Neural Networks in a Jupyter Notebook
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https://github.com/jay-johnson/antinex-core/blob/master/docker/notebooks/AntiNex-Using-Pre-Trained-Deep-Neural-Networks-For-Defense.ipynb

I have built the AntiNex v1 stack to run using docker-compose, but I will be exploring using kubernetes to host the containerized, distributed artificial intelligence stack next.

For those curious about how I found out that I needed to use scalers: I ended up trying to include volume within my regression stock price predictions while I was benchmarking scipype results with these new, terrible DNN results. I kept removing column after column until I noticed that having volume in the dataset caused my price predictions to be terrible... and the rest is what you see in AntiNex.

Thanks for reading!
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Until next time,

Jay



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