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

Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)

581 159 +0/wk

GitHub

autonomous-vehicles bayesian-optimization catboost cicids2017 cyber-security decision-tree ensemble-learning hpo hyperparameter-optimization intrusion-detection intrusion-detection-system kmeans

Trend 3

Star & Fork Trend (19 data points)

Stars

Forks

Multi-Source Signals

GitHub

stars 581

forks 159

Growth Velocity

Western-OC2-Lab/Intrusion-Detection-System-Using-Machine-Learning has +0 stars this period . 7-day velocity: 0.3%.

Deep analysis is being generated for this repository.

Signal-backed technical analysis will be available soon.

Metric	Intrusion-Detection-System-Using-Machine-Learning	seglearn	tuned-lens	Aspect-Based-Sentiment-Analysis
Stars	581	580	582	580
Forks	159	61	65	94
Weekly Growth	+0	+0	+0	+0
Language	Jupyter Notebook	Python	Python	Python
Sources	1	1	1	1
License	MIT	BSD-3-Clause	MIT	Apache-2.0

Capability Radar vs seglearn

Intrusion-Detection-System-Using-Machine-Learning

seglearn

Maintenance Activity 100

Last code push 7 days ago.

Community Engagement 100

Fork-to-star ratio: 27.4%. Active community forking and contributing.

Issue Burden 70

Issue data not yet available.

Growth Momentum 30

No measurable growth in the current period (first-day cold start expected).

License Clarity 95

Licensed under MIT. Permissive — safe for commercial use.

Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.