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Empower Network Analysts with Automated Pattern Discovery

Our modern lives are driven by computers. Machines grade your child’s standardized tests and identify potentially fraudulent credit card purchases. Behind the scenes of modern society even more machines are creating staggering amounts of unstructured data. Much of it is stored, but almost none of it is analyzed. Keeping these networks running requires that analysts not only be able to understand the streaming network data, but that they understand it as it happens.

Machine log data is generated by servers, routers, and other networked devices as they interact and manage processes and commands. Network security analysts are intimately familiar with the concept of machine log data, as the core of their job description is monitoring machine generated data for time-critical threats and machine failures.

The analysts tasked with monitoring streaming machine log data for network intrusion and fault indicators have seas of information flowing at them at once, and the tools available to detect and act on threats are manually intensive. Network analysts currently rely on querying sections of historical data to identify anomalous patterns similar to past downtime events. Meanwhile, threats change and evolve as fast as they come. Manual querying and the establishing of quickly outdated rules leaves holes in the ability to detect and act on threats as they happen.

Analysts need tools like automated pattern detection in their protective arsenal. These tools can pair with other network monitoring tools to continuously oversee network data, rank and list the most surprising patterns detected, and eliminate the noise that might mislead analysts. Automated pattern detection processes data in real time and stores historical data so that streaming data can be compared over time.

With automated pattern detection at their disposal, analysts are presented with a ranked list of the important and relevant events that demand attention instead of digging for them hopelessly. They can detect threats as they happen, preserving a potentially fragile balance between offensive and defensive measures to protect your network investments. Users can even prioritize events and set alerts to stay on top of the most critical threats to network performance.

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