GitHub / MohammedSaim-Quadri / Intrusion_Detection-System

This project is an Intrusion Detection System (IDS) using machine learning (ML) and deep learning (DL) to detect network intrusions. It leverages the CICIDS2018 dataset to classify traffic as normal or malicious. Key features include data preprocessing, model training, hyperparameter tuning, and Docker containerization for scalable deployment.

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Commit Stats

Commits: 17
Authors: 2
Mean commits per author: 8.5
Development Distribution Score: 0.412
More commit stats: https://commits.ecosyste.ms/hosts/GitHub/repositories/MohammedSaim-Quadri/Intrusion_Detection-System

Topics: bayesian-optimization, cicids2018, cybersecurity, datapreprocessing, deep-learning, docker, hyperparameter-tuning, intrusion-detection, machinelearning, neural-networks

• Files
• Readme
• Dependencies