Netflow Logic Corporation

Alexander Velednitsky

Igor Balabine

William G Friedman

The present invention relates to systems and methods for detecting anomalies in computer network traffic with fewer false positives and without the need for time-consuming and unreliable historical baselines. Upon detection, traffic anomalies can be processed to determine valuable network insights, including health of interfaces, devices and network services, as well as to provide timely alerts in the event of attack.

Method and system for confident anomaly detection in computer network traffic

A method and system for processing network metadata is described. Network metadata may be processed by dynamically instantiated executable software modules which make policy-based decisions about the character of the network metadata and about presentation of the network metadata to consumers of the information carried by the network metadata. The network metadata may be type classified and each subclass within a type may be mapped to a definition by a unique fingerprint value. The fingerprint value may be used for matching the network metadata subclasses against relevant policies and transformation rules. For template-based network metadata such as NetFlow v9, an embodiment of the invention can constantly monitor network traffic for unknown templates, capture template definitions, and informs administrators about templates for which custom policies and conversion rules do not exist. Conversion modules can efficiently convert selected types and/or subclasses of network metadata into alternative metadata formats.

Streaming method and system for processing network metadata

An improved method and system for processing network metadata is described. Network metadata may be processed by dynamically instantiated executable software modules which make policy-based decisions about the character of the network metadata and about presentation of the network metadata to consumers of the information carried by the network metadata. The network metadata may be type classified and each subclass within a type may be mapped to a definition by a unique fingerprint value. The fingerprint value may be used for matching the network metadata subclasses against relevant policies and transformation rules. For template-based network metadata such as NetFlow v9, an embodiment of the invention can constantly monitor network traffic for unknown templates, capture template definitions, and informs administrators about templates for which custom policies and conversion rules do not exist. Conversion modules can efficiently convert selected types and/or subclasses of network metadata into alternative metadata formats.

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Menlo Park, CA, United States of America

Alexander Velednitsky