The cybersecurity industry is increasingly producing enormous amounts of raw threat data. The sheer volume of information threat researchers must sift through makes it difficult to collect, analyze, and research that data in a timely manner. This in turn limits their ability to understand what data is valid and useful and whether threat artifacts will result in legitimate threat indicators.
This white paper will discuss the following topics:
- How data analytics and machine learning power threat analysis
- Machine learning models
- From threat artifact to threat intelligence
With this information of data analytics, automation, and machine learning models, threat researchers have the tools needed to help deal with the enormous (and growing) volume of threat data.
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