sleetymattgeorge/Deep-Learning-Evaluation-of-IDS-Datasets

Deep Model Intrusion Detection (IDS) Evaluation of NSL KDD and CIC IDS 2018 datasets.

27
/ 100
Experimental

This project helps cybersecurity researchers and network security analysts evaluate how well deep learning models can detect intrusions in network traffic. It takes standard intrusion detection datasets, specifically NSL KDD and CIC IDS 2018, and applies deep learning models to assess their performance in identifying cyber threats. The output provides insights into the effectiveness of these models for enhancing network security.

No commits in the last 6 months.

Use this if you are a cybersecurity researcher or analyst looking to benchmark deep learning models against established intrusion detection datasets to understand their efficacy.

Not ideal if you are a network administrator needing a live, deployable intrusion detection system for a production environment.

cybersecurity-research intrusion-detection network-security threat-analysis machine-learning-evaluation
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 5 / 25

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Stars

19

Forks

1

Language

Jupyter Notebook

License

MIT

Last pushed

May 10, 2023

Commits (30d)

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