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WIntrusion-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..)
Eproxelar
Scriptable local traffic workbench for inspecting, intercepting, replaying, and rewriting HTTP/HTTPS and WebSocket traffic.
Popularity
Repository stars6011,104Highest
7-day net stars——
Fork162Highest68
30-day trendInsufficient historyInsufficient history
Maintenance
Last push2026-04-012026-10-31
Maintenance notesNot verifiedNot verified
Repository created2021-05-252023-01-19
License & use
LicenseMIT · PermissiveMIT · Permissive
PricingPricing not verifiedPricing not verified
Basics
Browse by purposeNetwork securityCommand-line tools · Network security
TypeApps & systemsApps & systems
SubcategoryDeveloper toolsTerminals & command line
PlatformNot verifiedNot verified
Cost detailsCheck current official software, hosting, model and infrastructure pricing.Check current official software, hosting, model and infrastructure pricing.
Pricing source——
First collected2026-11-032026-11-03
MetricRepository stars, not unique users or individual-skill installsRepository stars, not unique users or individual-skill installs
LanguageJupyter NotebookRust
About this projectCode for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)Scriptable local traffic workbench for inspecting, intercepting, replaying, and rewriting HTTP/HTTPS and WebSocket traffic.
Measured at2026-11-02 19:02 UTC2026-11-02 19:02 UTC
License tiers are derived from metadata. Check obligations for your use; unknown does not mean prohibited. Ranked by repository stars, one position per repository. Stars and forks are not user counts.