vinayakumarr/Network-Intrusion-Detection

Network Intrusion Detection KDDCup '99', NSL-KDD and UNSW-NB15

43
/ 100
Emerging

Implements deep learning architectures including CNNs and RNNs for binary and multi-class intrusion classification across three benchmark datasets. The approach evaluates shallow versus deep neural networks on preprocessed network traffic features, comparing detection effectiveness across different model topologies and depths. Provides reproducible research implementations with documented performance metrics for standardized security datasets commonly used in IDS evaluation.

762 stars. No commits in the last 6 months.

No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 8 / 25
Community 25 / 25

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Stars

762

Forks

247

Language

Python

License

Last pushed

May 08, 2019

Commits (30d)

0

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