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AntiNex Core

Automating network exploit detection using highly accurate pre-trained deep neural networks.

As 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.

https://travis-ci.org/jay-johnson/antinex-core.svg?branch=master

Accuracy + Training + Cross Validation in a Jupyter Notebook

https://github.com/jay-johnson/antinex-core/blob/master/docker/notebooks/AntiNex-Protecting-Django.ipynb

Using a Pre-Trained Deep Neural Network in a Jupyter Notebook

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

Overview

The 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.

This repository is a standalone training and prediction worker pool that is decoupled from the AntiNex REST API:

https://github.com/jay-johnson/train-ai-with-django-swagger-jwt

AntiNex Stack Status

AntiNex Core Worker is part of the AntiNex stack:

Component Build Docs Link Docs Build
REST API Travis Tests Docs Read the Docs REST API Tests
Core Worker Travis AntiNex Core Tests Docs Read the Docs AntiNex Core Tests
Network Pipeline Travis AntiNex Network Pipeline Tests Docs Read the Docs AntiNex Network Pipeline Tests
AI Utils Travis AntiNex AI Utils Tests Docs Read the Docs AntiNex AI Utils Tests
Client Travis AntiNex Client Tests Docs Read the Docs AntiNex Client Tests

Install

pip install antinex-core

Optional for Generating Images

If you want to generate images please install python3-tk on Ubuntu.

sudo apt-get install python3-tk

Docker

Start the container for browsing with Jupyter:

# if you do not have docker compose installed, you can try installing it with:
# pip install docker-compose
cd docker
./start-stack.sh

Open Jupyter Notebook with Django Deep Neural Network Analysis

Default password is: admin

http://localhost:8888/notebooks/AntiNex-Protecting-Django.ipynb

View Notebook Presentation Slides

  1. Use Alt + r inside the notebook
  2. Use the non-vertical scolling url: http://localhost:8889/Slides-AntiNex-Protecting-Django.slides.html
  3. Use the non-vertical scolling url: http://localhost:8890/Slides-AntiNex-Using-Pre-Trained-Deep-Neural-Networks-For-Defense.slides.html

Run

Please make sure redis is running and accessible before starting the core:

redis-cli
127.0.0.1:6379>

With redis running and the antinex-core pip installed in the python 3 runtime, use this command to start the core:

./run-antinex-core.sh

Or with celery:

celery worker -A antinex_core.antinex_worker -l DEBUG

Publish a Predict Request

To 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.

Clone

Please make sure to clone the dataset repo to the pre-configured location:

mkdir -p -m 777 /opt/antinex
git clone https://github.com/jay-johnson/antinex-datasets.git /opt/antinex/antinex-datasets

Django - Train and Predict

./antinex_core/scripts/publish_predict_request.py -f training/scaler-full-django-antinex-simple.json

Flask - Train and Predict

./antinex_core/scripts/publish_predict_request.py -f training/scaler-full-flask-antinex-simple.json

React and Redux - Train and Predict

./antinex_core/scripts/publish_predict_request.py -f training/scaler-full-react-redux-antinex-simple.json

Vue - Train and Predict

./antinex_core/scripts/publish_predict_request.py -f training/scaler-full-vue-antinex-simple.json

Spring - Train and Predict

./antinex_core/scripts/publish_predict_request.py -f training/scaler-full-spring-antinex-simple.json

Accuracy and Prediction Report

After a few minutes the final report will be printed out like:

2018-03-11 23:35:00,944 - antinex-prc - INFO - sample=30178 - label_value=1.0 predicted=1 label=attack
2018-03-11 23:35:00,944 - antinex-prc - INFO - sample=30179 - label_value=-1.0 predicted=-1 label=not_attack
2018-03-11 23:35:00,944 - antinex-prc - INFO - sample=30180 - label_value=-1.0 predicted=-1 label=not_attack
2018-03-11 23:35:00,944 - antinex-prc - INFO - sample=30181 - label_value=-1.0 predicted=-1 label=not_attack
2018-03-11 23:35:00,944 - antinex-prc - INFO - sample=30182 - label_value=-1.0 predicted=-1 label=not_attack
2018-03-11 23:35:00,945 - antinex-prc - INFO - sample=30183 - label_value=-1.0 predicted=-1 label=not_attack
2018-03-11 23:35:00,945 - antinex-prc - INFO - sample=30184 - label_value=-1.0 predicted=-1 label=not_attack
2018-03-11 23:35:00,945 - antinex-prc - INFO - sample=30185 - label_value=-1.0 predicted=-1 label=not_attack
2018-03-11 23:35:00,945 - antinex-prc - INFO - sample=30186 - label_value=-1.0 predicted=-1 label=not_attack
2018-03-11 23:35:00,945 - antinex-prc - INFO - sample=30187 - label_value=-1.0 predicted=-1 label=not_attack
2018-03-11 23:35:00,945 - antinex-prc - INFO - sample=30188 - label_value=-1.0 predicted=-1 label=not_attack
2018-03-11 23:35:00,945 - antinex-prc - INFO - sample=30189 - label_value=1.0 predicted=1 label=attack
2018-03-11 23:35:00,945 - antinex-prc - INFO - sample=30190 - label_value=-1.0 predicted=-1 label=not_attack
2018-03-11 23:35:00,945 - antinex-prc - INFO - sample=30191 - label_value=-1.0 predicted=-1 label=not_attack
2018-03-11 23:35:00,946 - antinex-prc - INFO - sample=30192 - label_value=-1.0 predicted=-1 label=not_attack
2018-03-11 23:35:00,946 - antinex-prc - INFO - sample=30193 - label_value=-1.0 predicted=-1 label=not_attack
2018-03-11 23:35:00,946 - antinex-prc - INFO - sample=30194 - label_value=-1.0 predicted=-1 label=not_attack
2018-03-11 23:35:00,946 - antinex-prc - INFO - sample=30195 - label_value=-1.0 predicted=-1 label=not_attack
2018-03-11 23:35:00,946 - antinex-prc - INFO - sample=30196 - label_value=-1.0 predicted=-1 label=not_attack
2018-03-11 23:35:00,946 - antinex-prc - INFO - sample=30197 - label_value=-1.0 predicted=-1 label=not_attack
2018-03-11 23:35:00,946 - antinex-prc - INFO - sample=30198 - label_value=-1.0 predicted=-1 label=not_attack
2018-03-11 23:35:00,946 - antinex-prc - INFO - sample=30199 - label_value=-1.0 predicted=-1 label=not_attack
2018-03-11 23:35:00,947 - antinex-prc - INFO - Full-Django-AntiNex-Simple-Scaler-DNN made predictions=30200 found=30200 accuracy=99.84685430463577
2018-03-11 23:35:00,947 - antinex-prc - INFO - Full-Django-AntiNex-Simple-Scaler-DNN - saving model=full-django-antinex-simple-scaler-dnn

If you do not have the datasets cloned locally, you can use the included minimized dataset from the repo:

./antinex_core/scripts/publish_predict_request.py -f training/scaler-django-antinex-simple.json

Publish a Train Request

./antinex_core/scripts/publish_train_request.py

Publish a Regression Prediction Request

./antinex_core/scripts/publish_regression_predict.py

JSON API

The 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:

{
    "label": "Django-AntiNex-Simple-Scaler-DNN",
    "dataset": "./tests/datasets/classification/cleaned_attack_scans.csv",
    "apply_scaler": true,
    "ml_type": "classification",
    "predict_feature": "label_value",
    "features_to_process": [
        "eth_type",
        "idx",
        "ip_ihl",
        "ip_len",
        "ip_tos",
        "ip_version",
        "tcp_dport",
        "tcp_fields_options.MSS",
        "tcp_fields_options.Timestamp",
        "tcp_fields_options.WScale",
        "tcp_seq",
        "tcp_sport"
    ],
    "ignore_features": [
    ],
    "sort_values": [
    ],
    "seed": 42,
    "test_size": 0.2,
    "batch_size": 32,
    "epochs": 10,
    "num_splits": 2,
    "loss": "binary_crossentropy",
    "optimizer": "adam",
    "metrics": [
        "accuracy"
    ],
    "histories": [
        "val_loss",
        "val_acc",
        "loss",
        "acc"
    ],
    "model_desc": {
        "layers": [
            {
                "num_neurons": 250,
                "init": "uniform",
                "activation": "relu"
            },
            {
                "num_neurons": 1,
                "init": "uniform",
                "activation": "sigmoid"
            }
        ]
    },
    "label_rules": {
        "labels": [
            "not_attack",
            "not_attack",
            "attack"
        ],
        "label_values": [
            -1,
            0,
            1
        ]
    },
    "version": 1
}

Regression prediction tasks are also supported, and here is an example from an included dataset with mock stock prices:

{
    "label": "Scaler-Close-Regression",
    "dataset": "./tests/datasets/regression/stock.csv",
    "apply_scaler": true,
    "ml_type": "regression",
    "predict_feature": "close",
    "features_to_process": [
        "high",
        "low",
        "open",
        "volume"
    ],
    "ignore_features": [
    ],
    "sort_values": [
    ],
    "seed": 7,
    "test_size": 0.2,
    "batch_size": 32,
    "epochs": 50,
    "num_splits": 2,
    "loss": "mse",
    "optimizer": "adam",
    "metrics": [
        "accuracy"
    ],
    "model_desc": {
        "layers": [
            {
                "activation": "relu",
                "init": "uniform",
                "num_neurons": 200
            },
            {
                "activation": null,
                "init": "uniform",
                "num_neurons": 1
            }
        ]
    }
}

Splunk Environment Variables

This repository uses the Spylunking logger that supports publishing logs to Splunk over the authenticated HEC REST API. You can set these environment variables to publish to Splunk:

export SPLUNK_ADDRESS="<splunk address host:port>"
export SPLUNK_API_ADDRESS="<splunk api address host:port>"
export SPLUNK_USER="<splunk username for login>"
export SPLUNK_PASSWORD="<splunk password for login>"
export SPLUNK_TOKEN="<Optional - username and password will login or you can use a pre-existing splunk token>"
export SPLUNK_INDEX="<splunk index>"
export SPLUNK_QUEUE_SIZE="<num msgs allowed in queue - 0=infinite>"
export SPLUNK_RETRY_COUNT="<attempts per log to retry publishing>"
export SPLUNK_RETRY_BACKOFF="<cooldown in seconds per failed POST>"
export SPLUNK_SLEEP_INTERVAL="<sleep in seconds per batch>"
export SPLUNK_SOURCE="<splunk source>"
export SPLUNK_SOURCETYPE="<splunk sourcetype>"
export SPLUNK_TIMEOUT="<timeout in seconds>"
export SPLUNK_DEBUG="<1 enable debug|0 off - very verbose logging in the Splunk Publishers>"

Development

virtualenv -p python3 ~/.venvs/antinexcore && source ~/.venvs/antinexcore/bin/activate && pip install -e .

Testing

Run all

python setup.py test

Run a test case

python -m unittest tests.test_train.TestTrain.test_train_antinex_simple_success_retrain

Linting

flake8 .

pycodestyle .

License

Apache 2.0 - Please refer to the LICENSE for more details