Low-complexity detection of potential network anomalies using intermediate-stage processing
AssigneeLevel 3 Communications, LLC
InventorSergey Yermakov
Life of this patent
Estimating the term…
Abstract
In an embodiment, a computer implemented method receives flow data for a network flows. The method extracts a tuple from the flow data and calculates long-term and short-term trends based at least in part on the tuple. The long-term and short-term trends are compared to determine whether a potential network anomaly exists. If a potential network anomaly does exist, the method initiates a heavy hitter detection algorithm. The method forms a low-complexity intermediate stage of processing that enables a high-complexity heavy hitter detection algorithm to execute when heavy hitters are likely to be detected.
Claims18
A computer implemented method, comprising:
- receiving flow data for a network flow;
- parsing the flow data into a plurality of time buckets;
- extracting a plurality of tuples describing the flow data, the tuple comprising a time duration of the network flow and information identifying an amount of data transmitted during the flow;
- calculating a long-term trend based at least in part on at least a first tuple and two or more time buckets of the plurality of time buckets;
- calculating a short-term trend based at least in part on a second tuple and a most recent time bucket of the plurality of time buckets;
- determining that the short-term trend diverges from the long-term trend to detect a potential network anomaly, wherein determining that the short-term trend diverges from the long-term trend comprises determining that a percentage of tuples in a short-term cluster relative to other short-term clusters is significantly more than a percentage of tuples in a long-term cluster, corresponding to the short-term cluster, relative to other long-term clusters;
- and
- when the potential network anomaly is detected, initiating a heavy hitter detection algorithm.
A system, comprising:
- a memory;
- and
- at least one processor coupled to the memory and configured to:
- receive flow data for a network flow;
- parse the flow data into a plurality of time buckets;
- extract a plurality of tuples describing the flow data, wherein a tuple comprises a time duration of the network flow and information identifying an amount of data transmitted during the flow;
- calculate a long-term trend based at least in part on at least a first tuple and two or more time buckets of the plurality of time buckets;
- calculate a short-term trend based at least in part on a second tuple and a most recent time bucket of the plurality of time buckets;
- determining that the short-term trend diverges from the long-term trend to detect a potential network anomaly, wherein determining that the short-term trend diverges from the long-term trend comprises determining that a percentage of tuples in a short-term cluster relative to other short-term clusters is significantly more than a percentage of tuples in a long-term cluster, corresponding to the short-term cluster, relative to other long-term clusters;
- and
- when the potential network anomaly is detected, initiate a heavy hitter detection algorithm.
A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, causes the at least one computing device to perform operations comprising:
- receiving flow data for a network flow;
- parsing the flow data into a plurality of time buckets;
- extracting a plurality of tuples describing the flow data, wherein a tuple comprises a time duration of the network flow and information identifying an amount of data transmitted during the flow;
- calculating a long-term trend based at least in part on at least a first tuple and two or more time buckets of the plurality of time buckets;
- calculating a short-term trend based at least in part on a second tuple and a most recent time bucket of the plurality of time buckets;
- determining that the short-term trend diverges from the long-term trend to detect a potential network anomaly, wherein determining that the short-term trend diverges from the long-term trend comprises determining that a percentage of tuples in a short-term cluster relative to other short-term clusters is significantly more than a percentage of tuples in a long-term cluster, corresponding to the short-term cluster, relative to other long-term clusters;
- and
- when the potential network anomaly is detected, initiating a heavy hitter detection algorithm.
Dependencies are inferred from the claim text.