https://github.com/nadhirfr/cic-ids-2018

CSE-CIC-IDS-2018 analyze with Random Forest
https://github.com/nadhirfr/cic-ids-2018

data-analytics data-science ddos ddos-detection ddos-mitigation intrusion-detection intrusion-detection-system machine-learning random-forest

Last synced: 10 months ago
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CSE-CIC-IDS-2018 analyze with Random Forest

• Host: GitHub
• URL: https://github.com/nadhirfr/cic-ids-2018
• Owner: nadhirfr
• Created: 2019-07-06T08:14:06.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2021-04-04T19:30:17.000Z (about 5 years ago)
• Last Synced: 2025-04-05T10:33:36.003Z (about 1 year ago)
• Topics: data-analytics, data-science, ddos, ddos-detection, ddos-mitigation, intrusion-detection, intrusion-detection-system, machine-learning, random-forest
• Language: Jupyter Notebook
• Homepage:
• Size: 13.7 KB
• Stars: 32
• Watchers: 0
• Forks: 9
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Intrution-Detection-System with CSE-CIC-IDS-2018

This is machine learning analyze with random forest algorithm for CIC-IDS-2018. It used only "Thursday-15-02-2018_TrafficForML_CICFlowMeter.csv" files for analyzing DDoS attack. I applied the model for clasifying DDoS attack in Software-Defined Network with utilizing sFlow using Django + Django-Channels. 

Here for more : https://github.com/nadhirfr/rf-ids

[![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/H2H146AUD)

Credit:

- https://www.unb.ca/cic/datasets/ids-2018.html

- https://registry.opendata.aws/cse-cic-ids2018