![]() ![]() ![]() If improvements result from the latest damage patterns from the field, we can then upgrade the detection without gaps in the detection coverage. In the spirit of continuous improvement, we also test additional machine learning models and compare the results. By combining multiple models, both human intelligence and raw machine learning are combined for optimal results. For example, if the data is compressible and the replacement files are equally compressible, it’s unlikely that there’s been a crypto attack. We also have certain heuristic rule-based models written by humans to remove false positives. With a baseline of metrics established, our models start with an aggressive learning phase and transition to a steady-state that is continually optimized for precision and recall. Machine learning requires a baseline defining normal behavior, and Cohesity requires a minimum of 15 valid historical records to trigger a detection. ![]() The machine learning models are not AI-washing of things that could just as easily be done traditionally they are all multivariate models. Training sets representing patterns of changes generated by commonly used malware.Aggregated stats across multiple backups: Max data written bytes, max source logical size bytes, number of successful runs, and so on.Change tracking information per backup: Number of files added, files deleted, files updated, files unchanged.Content information per backup: Size of data written, size of data read, logical size.To detect ongoing attacks and avoid false positives, Cohesity feeds multiple metrics into machine learning algorithms running in our Helios control plane, including but not limited to: This is sometimes known as entropy detection because encrypted data looks very random, which is why it doesn’t compress. For example, when new data is suddenly less compressible than past work, that’s a potential hallmark of having been encrypted. Anomalous changes in backup data can provide accurate early warnings without generating a lot of false alarms. Keeping the Bad Guys at BayĪ distinct benefit of our backup process is intelligence gathering about the data and how it has changed. We provide advanced threat detection signals with fewer false positives, fast and efficient ways to receive, manage, and act efficiently on alerts both to stop the attack and to accelerate the cleanup. We’ve built on that foundation to help customers recognize an attack sooner and react faster. Our technology minimizes the attack surface and makes us better at meeting more stringent backup SLAs, dramatically speeding all manner of recovery operations. Why do customers use the Cohesity Data Cloud? It’s the last line of defense to protect, detect, and recover valuable data from ransomware attacks-quickly and safely. ![]()
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