From the description:
Metron integrates a variety of open source big data technologies in order to offer a centralized tool for security monitoring and analysis. Metron provides capabilities for log aggregation, full packet capture indexing, storage, advanced behavioral analytics and data enrichment, while applying the most current threat-intelligence information to security telemetry within a single platform.
Metron can be divided into 4 areas:
- A mechanism to capture, store, and normalize any type of security telemetry at extremely high rates. Because security telemetry is constantly being generated, it requires a method for ingesting the data at high speeds and pushing it to various processing units for advanced computation and analytics.
- Real time processing and application of enrichments such as threat intelligence, geolocation, and DNS information to telemetry being collected. The immediate application of this information to incoming telemetry provides the context and situational awareness, as well as the “who” and “where” information that is critical for investigation.
- Efficient information storage based on how the information will be used:
- Logs and telemetry are stored such that they can be efficiently mined and analyzed for concise security visibility
- The ability to extract and reconstruct full packets helps an analyst answer questions such as who the true attacker was, what data was leaked, and where that data was sent
- Long-term storage not only increases visibility over time, but also enables advanced analytics such as machine learning techniques to be used to create models on the information. Incoming data can then be scored against these stored models for advanced anomaly detection.
- An interface that gives a security investigator a centralized view of data and alerts passed through the system. Metron’s interface presents alert summaries with threat intelligence and enrichment data specific to that alert on one single page. Furthermore, advanced search capabilities and full packet extraction tools are presented to the analyst for investigation without the need to pivot into additional tools.
Big data is a natural fit for powerful security analytics. The Metron framework integrates a number of elements from the Hadoop ecosystem to provide a scalable platform for security analytics, incorporating such functionality as full-packet capture, stream processing, batch processing, real-time search, and telemetry aggregation. With Metron, our goal is to tie big data into security analytics and drive towards an extensible centralized platform to effectively enable rapid detection and rapid response for advanced security threats.
Some useful links:
Security threats aren’t going to assign themselves unique and immutable IDs. Which means they will be identified by characteristics and associated with particular acts (think associations), which are composed of other subjects, such as particular malware, dates, etc.
Being able to robustly share such identifications (unlike the “we’ve seen this before at some unknown time, with unknown characteristics,” typical of Russian attribution reports) would be a real plus.
Looks like a great opportunity for topic maps-like thinking.
Yes?