BBVA/Tarkin
A tool for anomaly detection over streaming data based on sentiment analysis
This tool helps security analysts quickly identify unusual and potentially malicious activities buried within vast streams of security logs. It takes raw log data as input and highlights messages that exhibit both infrequent character patterns and negative sentiment, presenting them as prioritized alerts. Security operations centers and incident response teams can use this to cut through the noise and focus on critical issues.
No commits in the last 6 months.
Use this if you need to rapidly detect security anomalies in real-time log data by mimicking a human analyst's intuition for spotting trouble.
Not ideal if your primary need is general log aggregation, simple keyword filtering, or if you require a system that explains its anomaly detection with highly detailed, transparent rules.
Stars
30
Forks
5
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Jul 02, 2018
Commits (30d)
0
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