Measurement-Based Validation of a Simple Model for Panoramic Profiling of Subnet-Level Network Data Traffic
First Claim
1. A method of profiling network traffic comprising:
- determining a probabilistic classification of a plurality of subnets into a plurality of clusters based on at least one network traffic feature; and
deriving a network profile using said probabilistic classification and traffic measurement data associated with at least one of said plurality of subnets.
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Abstract
A system and method for profiling subnet-level aggregate network data traffic is disclosed. The system allows a user to define a collection of features that combined characterize the subnet-level aggregate traffic behavior. Preferably, the features include daily traffic volume, time-of-day behavior, spatial traffic distribution, traffic balance in flow direction, and traffic distribution in type of application. The system then applies machine learning techniques to classify the subnets into a number of clusters on each of the features, by assigning a membership probability vector to each network thus allowing panoramic traffic profiles to be created for each network on all features combined. These membership probability vectors may optionally be used to detect network anomalies, or to predict future network traffic.
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Citations
20 Claims
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1. A method of profiling network traffic comprising:
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determining a probabilistic classification of a plurality of subnets into a plurality of clusters based on at least one network traffic feature; and deriving a network profile using said probabilistic classification and traffic measurement data associated with at least one of said plurality of subnets. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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- 10. A system for profiling network traffic comprising a computing device, the computing device being configured to probabilistically classify a plurality of subnets into a plurality of clusters based on at least one network traffic feature, the computing device being configured to derive a network profile in response to receiving traffic measurement data associated with at least one of said subnets.
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19. A computer readable medium comprising instructions executable by a computing device that, when applied to the computing device, cause the device to:
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determine a probabilistic classification of a plurality of subnets into a plurality of clusters based on at least one network traffic feature; and derive a network profile in using said probabilistic classification and traffic measurement data associated with at least one of said plurality of subnets. - View Dependent Claims (20)
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Specification