bibs2091 / Anomaly-detection-system

Machine learning based Intrusion detection system (IDS)

Anomaly-detection

1-INTRODUCTION:

In this project we propose a solution for the cyber attacks on networks as a machine learning based Intrusion detection system(IDS) and it's splitted to two parts:

1.1- Capturing the network flows and Features extraction:

In this part we use CICFlowMeter to capture the flow and extract the features (83 feature),and tweaked its ui for a simpler usage.

1.2- Prediction:

In order to achieve the highest accuracy we splitted the task into two stages to respectively detect the anomaly then classify it to a list of attacks we trained our model on, we used CSE-CIC-IDS2018 database to assure that we have the latest possible data on current cyber attacks.

2-Requirements:

• 2.1-For the Prediction Model:

  requirements.txt contains the needed python libraries. The needed python verson should be python3.*.

  $ pip install -r model/requirements.txt 
  

• 2.2-For CICFlowMeter:

  • Java jdk
  • check CICFlowMeter-master/README.md

3-Running the project

3.1-Start the model server

$ python model/app.py

3.2-Start CICFlowMeter

3.2.1-For Linux

cd CICFlowMeter-master/
sudo gradle

4.2.2-For Windows

dir CICFlowMeter-master/
./gradlew execute