alik604/cyber-security

Machine Learning for Network Intrusion Detection & Misc Cyber Security Utilities

/ 100

Emerging

This project helps network security professionals and data analysts automatically identify unusual and potentially malicious activities within network traffic. It takes raw network connection data and flags connections that deviate significantly from normal patterns, helping to detect intrusions. The main users are cybersecurity analysts, IT security managers, and network operations engineers who need to monitor network health.

222 stars. No commits in the last 6 months.

Use this if you need to build or evaluate machine learning models for detecting cyber threats and anomalies within network data, especially for intrusion detection.

Not ideal if you're looking for a deployable, ready-to-use commercial intrusion detection system or a tool for active network defense.

network-security intrusion-detection anomaly-detection cybersecurity-analysis network-monitoring

No License Stale 6m No Package No Dependents

Maintenance 0 / 25

Adoption 10 / 25

Maturity 8 / 25

Community 22 / 25

How are scores calculated?

Stars

222

Forks

Language

Jupyter Notebook

License

—

Higher-rated alternatives

AIS-Package/aisp

Artificial Immune Systems Package (AISP) is an open-source Python library that features...

ubc-provenance/PIDSMaker

A framework for building provenance-based intrusion detection systems with neural networks

Western-OC2-Lab/Intrusion-Detection-System-Using-Machine-Learning

Code for IDS-ML: intrusion detection system development using machine learning algorithms...

zimingttkx/Network-Security-Based-On-ML

abhinav-bhardwaj/Network-Intrusion-Detection-Using-Machine-Learning

A Novel Statistical Analysis and Autoencoder Driven Intelligent Intrusion Detection Approach

Explore ML Frameworks

All categories Trending ML Framework directory Insights