Network Anomaly Detection…and more
NetNous ensures IT infrastructure performance through automatic anomaly detection. Use LSTech's powerful AI & ML algorithms to discover patterns in your IT infrastructure operation, set up alarms to detect anomalous behaviors, create insightful visualizations, and conduct root-cause analysis connecting to powerful analytics platforms
What IS NETNOUS
Similar to a doctor’s stethoscope and thermometer NetNous provides information about the health of your network, servers, and applications simply by monitoring IP packets.
NetNous is used, among others, for intrusion detection thus can also be used for extrusion detection in order to provide hints for potential dataset leakages from untrusted Cloud-hosted Data Marketplaces.
How It Works
BENEFITS
An indispensable tool for all operations and system admin staff that helps them discover:
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addition/removal of
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clients on existing apps and resources
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apps on existing resources
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resources for same clients and apps
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flow-tracing tool malfunctions
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careless clients
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lousy-built apps
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malevolent attacks
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Key FeatureS
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Interpretable evolution assessments
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Minimize false negatives (i.e. miss a true alarm)
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Fully configurable to any need
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from time-zones and alarming metrics to model algorithms and parameters
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On-premise and cloud-based deployment
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flexible exec-environment description
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transparent code instantiation and patching
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SUCCESS STORY
Major telecommunications provider in SE Europe
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Processes 25K flow-aggregates per 1 min
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Operating every 6 mins, responding within 2 mins after
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Excellent balance between unsupervised learning & heuristic rules
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Impressive reduction of false positive alarms thanks to model interpretability
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Well-appreciated insights, thanks to rich but ergonomic dashboards
Request a demo
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Our team of data scientists is available to show you the features of our innovative product and to answer your questions.
We care about your privacy and will always treat your data with respect (privacy policy).
Confidentiality assured. Your information is never shared