{
    "info": {
        "author": "Jay Johnson",
        "author_email": "jay.p.h.johnson@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: PyPy",
            "Topic :: Software Development :: Libraries :: Python Modules"
        ],
        "description": "AntiNex Core\n============\n\nAutomating network exploit detection using highly accurate pre-trained deep neural networks.\n\nAs of 2018-03-12, the core can repeatedly predict attacks on Django, Flask, React + Redux, Vue, and Spring application servers by training using the pre-recorded `AntiNex datasets`_ with cross validation scores above **~99.8%** with automated scaler normalization.\n\n.. image:: https://travis-ci.org/jay-johnson/antinex-core.svg?branch=master\n    :target: https://travis-ci.org/jay-johnson/antinex-core\n\nAccuracy + Training + Cross Validation in a Jupyter Notebook\n------------------------------------------------------------\n\nhttps://github.com/jay-johnson/antinex-core/blob/master/docker/notebooks/AntiNex-Protecting-Django.ipynb\n\nUsing a Pre-Trained Deep Neural Network in a Jupyter Notebook\n-------------------------------------------------------------\n\nhttps://github.com/jay-johnson/antinex-core/blob/master/docker/notebooks/AntiNex-Using-Pre-Trained-Deep-Neural-Networks-For-Defense.ipynb\n\n.. _AntiNex datasets: https://github.com/jay-johnson/antinex-datasets\n\nOverview\n--------\n\nThe core is a Celery worker pool for processing training and prediction requests for deep neural networks to detect network exploits (Nex) using Keras and Tensorflow in near real-time. Internally each worker manages a buffer of pre-trained models identified by the ``label`` from the initial training request. Once trained, a model can be used for rapid prediction testing provided the same ``label`` name is used on the prediction request. Models can also be re-trained by using the training api with the same ``label``. While the initial focus is on network exploits, the repository also includes mock stock data for demonstrating running a worker pool to quickly predict regression data (like stock prices) with many, pre-trained deep neural networks.\n\nThis repository is a standalone training and prediction worker pool that is decoupled from the AntiNex REST API:\n\nhttps://github.com/jay-johnson/train-ai-with-django-swagger-jwt\n\nAntiNex Stack Status\n--------------------\n\nAntiNex Core Worker is part of the AntiNex stack:\n\n.. list-table::\n   :header-rows: 1\n\n   * - Component\n     - Build\n     - Docs Link\n     - Docs Build\n   * - `REST API <https://github.com/jay-johnson/train-ai-with-django-swagger-jwt>`__\n     - .. image:: https://travis-ci.org/jay-johnson/train-ai-with-django-swagger-jwt.svg?branch=master\n           :alt: Travis Tests\n           :target: https://travis-ci.org/jay-johnson/train-ai-with-django-swagger-jwt.svg\n     - `Docs <http://antinex.readthedocs.io/en/latest/>`__\n     - .. image:: https://readthedocs.org/projects/antinex/badge/?version=latest\n           :alt: Read the Docs REST API Tests\n           :target: https://readthedocs.org/projects/antinex/badge/?version=latest\n   * - `Core Worker <https://github.com/jay-johnson/antinex-core>`__\n     - .. image:: https://travis-ci.org/jay-johnson/antinex-core.svg?branch=master\n           :alt: Travis AntiNex Core Tests\n           :target: https://travis-ci.org/jay-johnson/antinex-core.svg\n     - `Docs <http://antinex-core-worker.readthedocs.io/en/latest/>`__\n     - .. image:: https://readthedocs.org/projects/antinex-core-worker/badge/?version=latest\n           :alt: Read the Docs AntiNex Core Tests\n           :target: http://antinex-core-worker.readthedocs.io/en/latest/?badge=latest\n   * - `Network Pipeline <https://github.com/jay-johnson/network-pipeline>`__\n     - .. image:: https://travis-ci.org/jay-johnson/network-pipeline.svg?branch=master\n           :alt: Travis AntiNex Network Pipeline Tests\n           :target: https://travis-ci.org/jay-johnson/network-pipeline.svg\n     - `Docs <http://antinex-network-pipeline.readthedocs.io/en/latest/>`__\n     - .. image:: https://readthedocs.org/projects/antinex-network-pipeline/badge/?version=latest\n           :alt: Read the Docs AntiNex Network Pipeline Tests\n           :target: https://readthedocs.org/projects/antinex-network-pipeline/badge/?version=latest\n   * - `AI Utils <https://github.com/jay-johnson/antinex-utils>`__\n     - .. image:: https://travis-ci.org/jay-johnson/antinex-utils.svg?branch=master\n           :alt: Travis AntiNex AI Utils Tests\n           :target: https://travis-ci.org/jay-johnson/antinex-utils.svg\n     - `Docs <http://antinex-ai-utilities.readthedocs.io/en/latest/>`__\n     - .. image:: https://readthedocs.org/projects/antinex-ai-utilities/badge/?version=latest\n           :alt: Read the Docs AntiNex AI Utils Tests\n           :target: http://antinex-ai-utilities.readthedocs.io/en/latest/?badge=latest\n   * - `Client <https://github.com/jay-johnson/antinex-client>`__\n     - .. image:: https://travis-ci.org/jay-johnson/antinex-client.svg?branch=master\n           :alt: Travis AntiNex Client Tests\n           :target: https://travis-ci.org/jay-johnson/antinex-client.svg\n     - `Docs <http://antinex-client.readthedocs.io/en/latest/>`__\n     - .. image:: https://readthedocs.org/projects/antinex-client/badge/?version=latest\n           :alt: Read the Docs AntiNex Client Tests\n           :target: https://readthedocs.org/projects/antinex-client/badge/?version=latest\n\nInstall\n-------\n\npip install antinex-core\n\nOptional for Generating Images\n------------------------------\n\nIf you want to generate images please install ``python3-tk`` on Ubuntu.\n\n::\n\n    sudo apt-get install python3-tk\n\nDocker\n------\n\nStart the container for browsing with Jupyter:\n\n::\n\n    # if you do not have docker compose installed, you can try installing it with:\n    # pip install docker-compose\n    cd docker\n    ./start-stack.sh\n\nOpen Jupyter Notebook with Django Deep Neural Network Analysis\n--------------------------------------------------------------\n\nDefault password is: ``admin``\n\nhttp://localhost:8888/notebooks/AntiNex-Protecting-Django.ipynb\n\nView Notebook Presentation Slides\n---------------------------------\n\n#.  Use ``Alt + r`` inside the notebook\n\n#.  Use the non-vertical scolling url: http://localhost:8889/Slides-AntiNex-Protecting-Django.slides.html\n\n#.  Use the non-vertical scolling url: http://localhost:8890/Slides-AntiNex-Using-Pre-Trained-Deep-Neural-Networks-For-Defense.slides.html\n\nRun\n---\n\nPlease make sure redis is running and accessible before starting the core:\n\n::\n\n    redis-cli \n    127.0.0.1:6379>\n\nWith redis running and the antinex-core pip installed in the python 3 runtime, use this command to start the core:\n\n::\n\n    ./run-antinex-core.sh\n\nOr with celery:\n\n::\n\n    celery worker -A antinex_core.antinex_worker -l DEBUG\n\nPublish a Predict Request\n-------------------------\n\nTo train and predict with the new automated scaler-normalized dataset with a 99.8% prediction accuracy for detecting attacks using a wide, two-layer deep neural network with the `AntiNex datasets`_ run the following steps.\n\n.. _AntiNex datasets: https://github.com/jay-johnson/antinex-datasets\n\nClone\n-----\n\nPlease make sure to clone the dataset repo to the pre-configured location:\n\n::\n\n    mkdir -p -m 777 /opt/antinex\n    git clone https://github.com/jay-johnson/antinex-datasets.git /opt/antinex/antinex-datasets\n\nDjango - Train and Predict\n--------------------------\n\n::\n\n    ./antinex_core/scripts/publish_predict_request.py -f training/scaler-full-django-antinex-simple.json\n\nFlask - Train and Predict\n-------------------------\n\n::\n\n    ./antinex_core/scripts/publish_predict_request.py -f training/scaler-full-flask-antinex-simple.json\n\nReact and Redux - Train and Predict\n-----------------------------------\n\n::\n\n    ./antinex_core/scripts/publish_predict_request.py -f training/scaler-full-react-redux-antinex-simple.json\n\nVue - Train and Predict\n-----------------------\n\n::\n\n    ./antinex_core/scripts/publish_predict_request.py -f training/scaler-full-vue-antinex-simple.json\n\nSpring - Train and Predict\n--------------------------\n\n::\n\n    ./antinex_core/scripts/publish_predict_request.py -f training/scaler-full-spring-antinex-simple.json\n\nAccuracy and Prediction Report\n------------------------------\n\nAfter a few minutes the final report will be printed out like:\n\n::\n\n    2018-03-11 23:35:00,944 - antinex-prc - INFO - sample=30178 - label_value=1.0 predicted=1 label=attack\n    2018-03-11 23:35:00,944 - antinex-prc - INFO - sample=30179 - label_value=-1.0 predicted=-1 label=not_attack\n    2018-03-11 23:35:00,944 - antinex-prc - INFO - sample=30180 - label_value=-1.0 predicted=-1 label=not_attack\n    2018-03-11 23:35:00,944 - antinex-prc - INFO - sample=30181 - label_value=-1.0 predicted=-1 label=not_attack\n    2018-03-11 23:35:00,944 - antinex-prc - INFO - sample=30182 - label_value=-1.0 predicted=-1 label=not_attack\n    2018-03-11 23:35:00,945 - antinex-prc - INFO - sample=30183 - label_value=-1.0 predicted=-1 label=not_attack\n    2018-03-11 23:35:00,945 - antinex-prc - INFO - sample=30184 - label_value=-1.0 predicted=-1 label=not_attack\n    2018-03-11 23:35:00,945 - antinex-prc - INFO - sample=30185 - label_value=-1.0 predicted=-1 label=not_attack\n    2018-03-11 23:35:00,945 - antinex-prc - INFO - sample=30186 - label_value=-1.0 predicted=-1 label=not_attack\n    2018-03-11 23:35:00,945 - antinex-prc - INFO - sample=30187 - label_value=-1.0 predicted=-1 label=not_attack\n    2018-03-11 23:35:00,945 - antinex-prc - INFO - sample=30188 - label_value=-1.0 predicted=-1 label=not_attack\n    2018-03-11 23:35:00,945 - antinex-prc - INFO - sample=30189 - label_value=1.0 predicted=1 label=attack\n    2018-03-11 23:35:00,945 - antinex-prc - INFO - sample=30190 - label_value=-1.0 predicted=-1 label=not_attack\n    2018-03-11 23:35:00,945 - antinex-prc - INFO - sample=30191 - label_value=-1.0 predicted=-1 label=not_attack\n    2018-03-11 23:35:00,946 - antinex-prc - INFO - sample=30192 - label_value=-1.0 predicted=-1 label=not_attack\n    2018-03-11 23:35:00,946 - antinex-prc - INFO - sample=30193 - label_value=-1.0 predicted=-1 label=not_attack\n    2018-03-11 23:35:00,946 - antinex-prc - INFO - sample=30194 - label_value=-1.0 predicted=-1 label=not_attack\n    2018-03-11 23:35:00,946 - antinex-prc - INFO - sample=30195 - label_value=-1.0 predicted=-1 label=not_attack\n    2018-03-11 23:35:00,946 - antinex-prc - INFO - sample=30196 - label_value=-1.0 predicted=-1 label=not_attack\n    2018-03-11 23:35:00,946 - antinex-prc - INFO - sample=30197 - label_value=-1.0 predicted=-1 label=not_attack\n    2018-03-11 23:35:00,946 - antinex-prc - INFO - sample=30198 - label_value=-1.0 predicted=-1 label=not_attack\n    2018-03-11 23:35:00,946 - antinex-prc - INFO - sample=30199 - label_value=-1.0 predicted=-1 label=not_attack\n    2018-03-11 23:35:00,947 - antinex-prc - INFO - Full-Django-AntiNex-Simple-Scaler-DNN made predictions=30200 found=30200 accuracy=99.84685430463577\n    2018-03-11 23:35:00,947 - antinex-prc - INFO - Full-Django-AntiNex-Simple-Scaler-DNN - saving model=full-django-antinex-simple-scaler-dnn\n\n\nIf you do not have the datasets cloned locally, you can use the included minimized dataset from the repo:\n\n::\n\n    ./antinex_core/scripts/publish_predict_request.py -f training/scaler-django-antinex-simple.json\n\nPublish a Train Request\n-----------------------\n\n::\n\n    ./antinex_core/scripts/publish_train_request.py\n\nPublish a Regression Prediction Request\n---------------------------------------\n\n::\n\n    ./antinex_core/scripts/publish_regression_predict.py\n\nJSON API\n--------\n\nThe AntiNex core manages a pool of workers that are subscribed to process tasks found in two queues (``webapp.train.requests`` and ``webapp.predict.requests``). Tasks are defined as JSON dictionaries and must have the following structure:\n\n::\n\n    {\n        \"label\": \"Django-AntiNex-Simple-Scaler-DNN\",\n        \"dataset\": \"./tests/datasets/classification/cleaned_attack_scans.csv\",\n        \"apply_scaler\": true,\n        \"ml_type\": \"classification\",\n        \"predict_feature\": \"label_value\",\n        \"features_to_process\": [\n            \"eth_type\",\n            \"idx\",\n            \"ip_ihl\",\n            \"ip_len\",\n            \"ip_tos\",\n            \"ip_version\",\n            \"tcp_dport\",\n            \"tcp_fields_options.MSS\",\n            \"tcp_fields_options.Timestamp\",\n            \"tcp_fields_options.WScale\",\n            \"tcp_seq\",\n            \"tcp_sport\"\n        ],\n        \"ignore_features\": [\n        ],\n        \"sort_values\": [\n        ],\n        \"seed\": 42,\n        \"test_size\": 0.2,\n        \"batch_size\": 32,\n        \"epochs\": 10,\n        \"num_splits\": 2,\n        \"loss\": \"binary_crossentropy\",\n        \"optimizer\": \"adam\",\n        \"metrics\": [\n            \"accuracy\"\n        ],\n        \"histories\": [\n            \"val_loss\",\n            \"val_acc\",\n            \"loss\",\n            \"acc\"\n        ],\n        \"model_desc\": {\n            \"layers\": [\n                {\n                    \"num_neurons\": 250,\n                    \"init\": \"uniform\",\n                    \"activation\": \"relu\"\n                },\n                {\n                    \"num_neurons\": 1,\n                    \"init\": \"uniform\",\n                    \"activation\": \"sigmoid\"\n                }\n            ]\n        },\n        \"label_rules\": {\n            \"labels\": [\n                \"not_attack\",\n                \"not_attack\",\n                \"attack\"\n            ],\n            \"label_values\": [\n                -1,\n                0,\n                1\n            ]\n        },\n        \"version\": 1\n    }\n\nRegression prediction tasks are also supported, and here is an example from an included dataset with mock stock prices:\n\n::\n\n    {\n        \"label\": \"Scaler-Close-Regression\",\n        \"dataset\": \"./tests/datasets/regression/stock.csv\",\n        \"apply_scaler\": true,\n        \"ml_type\": \"regression\",\n        \"predict_feature\": \"close\",\n        \"features_to_process\": [\n            \"high\",\n            \"low\",\n            \"open\",\n            \"volume\"\n        ],\n        \"ignore_features\": [\n        ],\n        \"sort_values\": [\n        ],\n        \"seed\": 7,\n        \"test_size\": 0.2,\n        \"batch_size\": 32,\n        \"epochs\": 50,\n        \"num_splits\": 2,\n        \"loss\": \"mse\",\n        \"optimizer\": \"adam\",\n        \"metrics\": [\n            \"accuracy\"\n        ],\n        \"model_desc\": {\n            \"layers\": [\n                {\n                    \"activation\": \"relu\",\n                    \"init\": \"uniform\",\n                    \"num_neurons\": 200\n                },\n                {\n                    \"activation\": null,\n                    \"init\": \"uniform\",\n                    \"num_neurons\": 1\n                }\n            ]\n        }\n    }\n\nSplunk Environment Variables\n----------------------------\n\nThis repository uses the `Spylunking <https://github.com/jay-johnson/spylunking>`__ logger that supports publishing logs to Splunk over the authenticated HEC REST API. You can set these environment variables to publish to Splunk:\n\n::\n\n    export SPLUNK_ADDRESS=\"<splunk address host:port>\"\n    export SPLUNK_API_ADDRESS=\"<splunk api address host:port>\"\n    export SPLUNK_USER=\"<splunk username for login>\"\n    export SPLUNK_PASSWORD=\"<splunk password for login>\"\n    export SPLUNK_TOKEN=\"<Optional - username and password will login or you can use a pre-existing splunk token>\"\n    export SPLUNK_INDEX=\"<splunk index>\"\n    export SPLUNK_QUEUE_SIZE=\"<num msgs allowed in queue - 0=infinite>\"\n    export SPLUNK_RETRY_COUNT=\"<attempts per log to retry publishing>\"\n    export SPLUNK_RETRY_BACKOFF=\"<cooldown in seconds per failed POST>\"\n    export SPLUNK_SLEEP_INTERVAL=\"<sleep in seconds per batch>\"\n    export SPLUNK_SOURCE=\"<splunk source>\"\n    export SPLUNK_SOURCETYPE=\"<splunk sourcetype>\"\n    export SPLUNK_TIMEOUT=\"<timeout in seconds>\"\n    export SPLUNK_DEBUG=\"<1 enable debug|0 off - very verbose logging in the Splunk Publishers>\"\n\nDevelopment\n-----------\n::\n\n    virtualenv -p python3 ~/.venvs/antinexcore && source ~/.venvs/antinexcore/bin/activate && pip install -e .\n\nTesting\n-------\n\nRun all\n\n::\n\n    python setup.py test\n\nRun a test case\n\n::\n\n    python -m unittest tests.test_train.TestTrain.test_train_antinex_simple_success_retrain\n\nLinting\n-------\n\nflake8 .\n\npycodestyle .\n\nLicense\n-------\n\nApache 2.0 - Please refer to the LICENSE_ for more details\n\n.. _License: https://github.com/jay-johnson/antinex-core/blob/master/LICENSE\n\n\n",
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