bhanukaManesha / brain-inspired-user-behavioural-traffic-monitoring-system

Contains an implementation using NuPIC HTM model for Network Anomaly Detection. This uses the Hierarchical Temporal Memory Architecture to detect anomalies in networks.

Brain Inspired User Behavioural Traffic Monitoring System - MSC APICTA 2019

Numenta HTM model for Network Anomaly Detection

Production Server Setup Process

# Build the docker image
docker build -t apicta .

# Run the production server inside the docker container
# This will run the server on port 3000
docker run -p 3000:80 apicta

Accessing the API

Send a POST request this route to get the prediction

localhost:3000/data

Use this format for the api calls.

{"data": [
    {
      "timestamp": 1331901000.0,
      "total": 17312,
      "total_tcp": 16891,
      "total_http": 422,
      "total_udp": 173,
      "size": 2558408,
      "size_tcp": 2528168,
      "size_http": 76414,
      "size_udp": 12279
    }
  ]
 }

Do note that mutiple time stamps are also valid

{"data": [
    {
      "timestamp": 1331901000.0,
      "total": 17312,
      "total_tcp": 16891,
      "total_http": 422,
      "total_udp": 173,
      "size": 2558408,
      "size_tcp": 2528168,
      "size_http": 76414,
      "size_udp": 12279
    },
    {
      "timestamp": 1331901000.0,
      "total": 17312,
      "total_tcp": 16891,
      "total_http": 422,
      "total_udp": 173,
      "size": 2558408,
      "size_tcp": 2528168,
      "size_http": 76414,
      "size_udp": 12279
    },
    {
      "timestamp": 1331901000.0,
      "total": 17312,
      "total_tcp": 16891,
      "total_http": 422,
      "total_udp": 173,
      "size": 2558408,
      "size_tcp": 2528168,
      "size_http": 76414,
      "size_udp": 12279
    }
  ]
 }
``