slrbl/Intrusion-and-anomaly-detection-with-machine-learning
Machine learning algorithms applied on log analysis to detect intrusions and suspicious activities.
This tool helps cybersecurity teams automatically detect web attacks and suspicious activities in application logs, like HTTP traffic. It takes raw log files as input, analyzes them using machine learning to find unusual patterns, and then provides detailed reports and actionable recommendations on potential threats. Security Operations Center (SOC) analysts or IT security personnel would use this to enhance their intrusion detection capabilities.
171 stars.
Use this if you need an automated, rule-free system to identify novel web attack traces and anomalies in your application logs, complete with AI-powered analysis.
Not ideal if you primarily rely on signature-based detection for known threats or require a fully managed, cloud-based security solution with minimal self-hosting.
Stars
171
Forks
75
Language
Python
License
MIT
Category
Last pushed
Nov 06, 2025
Commits (30d)
0
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