ychalier / anomaly

Classification anomaly detection in IOT with Machine Learning

anomaly

Classification anomaly detection in IOT with Machine Learning

See this notebook for an example of our work.

setup

use a virtual environment with Python 3.6 and install the modules from requirements.txt.

python -m virtualenv venv
source venv/bin/activate
pip install -r requirements.txt

usage

You only need two files, detector.py contains the Detector that is used to store and compute datasets. A example of its usage is shown in main.ipynb.

• body.csv (source: Grete Heinz, Louis J. Peterson, Roger W. Johnson, and Carter J. Kerk. Exploring relationships in body dimensions. Journal of Statistics Education, Volume 11, Number 2)
• moto.csv (sample from the Motorcycle database from Teoalida, which is a gathering of data scrapped of bikez.com)
• moto2.csv (sample from motocicliste.net)

One may use scp to copy databases over a distant server to execute code.

scp *.csv user@host:path/

parameters tuning

In order to find the best parameters for each classifier, we use a grid search with cross validation to find the best set of parameters. The class Detector now has a method called tune_parameters which seeks the best set of selected parameters for a given classifier. This method is used in a small Python script called parameters.py that can be run on more powerful servers.

This script outputs a .out file that then can be exported as JSON and/or CSV using beautify.py. You can find examples of those files here.

CSV files are formatted such that a single test batch is split in several files:

• .scores.csv: the detailed scores for each test (including the confusion matrix), along with the unique parameters combination id.
• .params.estimatorName.csv: for each type of classifier, the value of each parameter for every combination using this classifier, along with the combination id used as a foreign key with the scores table.

Side note: the delimiter for those CSV file is currently ;.

The script process_output.py processes thoses CSV files to produce relevant plots. Here is an example:

usage in production

The following notebook shows how to generate the prediction function. Then, the following script only need this file to classify whether the given record is an anomaly or not:

def classify(class_, length, width):
  import pickle
  file = open('anomaly_classifier.clf', 'rb')
  fun = pickle.load(file)
  file.close()
  if fun(class_, length, width):
      return "malicious"
  else:
      return "non-malicous"