NETWORK ACTIVITY ANOMALY DETECTION

• US 20090180391A1
• Filed: 01/16/2008
• Published: 07/16/2009
• Est. Priority Date: 01/16/2008
• Status: Abandoned Application

• Current Assignee: Avago Technologies General IP PTE Limited (Broadcom, Inc.)
• Original Assignee: Broadcom Corporation (Broadcom, Inc.)
• Inventors: Chung, Edgar, Petersen, Brian
• Application Number:
  US12/015,387
• Publication Number:
  US 20090180391A1
• Time in Patent Office: Days
• US Class Current: 370/252
• CPC Class Codes: H04L 41/142 using statistical or mathematical methods
• CPC Class Codes: H04L 41/5022 by giving priorities, e.g. assigning classes of service
• CPC Class Codes: H04L 41/5025 by proactively reacting to service quality change, e.g. by reconfiguration after service quality degradation or upgrade
• CPC Class Codes: H04L 43/0876 Network utilisation, e.g. volume of load or congestion level
• CPC Class Codes: H04L 43/16 Threshold monitoring

Abstract

A method for determining whether anomalous activity exists on a network includes receiving a packet from the network, the packet including one or more fields. A classification of the packet based on the one or more fields is determined. A first counter of one or more counters associated with detecting the anomalous activity is incremented based on the classification. An activity metric associated with the one or more counters is determined based on the incrementing, wherein the activity metric is anticipated to fall within a threshold. Whether the anomalous activity exists on the network is determined based on whether the activity metric falls within the threshold.

Claims

1. A method for determining whether anomalous activity exists on a network, comprising:

receiving a packet from the network, the packet including one or more fields;

determining a classification of the packet based on the one or more fields;

incrementing, based on the classification, a first counter of one or more counters associated with detecting the anomalous activity;

determining, based on the incrementing, an activity metric associated with the one or more counters wherein the activity metric is anticipated to fall within a threshold; and

determining whether the anomalous activity exists on the network based on whether the activity metric falls within the threshold.

2. The method of claim 1, wherein determining the classification comprises comparing the one or more fields to one or more classification rules associated with the classification.

3. The method of claim 1 wherein, incrementing the first counter comprises:

receiving a first packet from the network, the first packet being associated with a first classification;

receiving a second packet from the network, the second packet being associated with the first classification;

determining a rate between the receiving the first packet and the receiving the second packet; and

tracking the rate via the first counter.

4. The method of claim 1, wherein incrementing the first counter comprises signaling the first counter to increment.

5. The method of claim 1, wherein incrementing the counter comprises:

determining, based on the classification, that the first counter is associated with the packet; and

incrementing the first counter.

6. The method of claim 1, wherein determining the activity metric comprises determining a difference between two or more of the counters.

7. The method of claim 1, wherein determining the activity metric comprises determining a ratio between two or more of the counters.

8. The method of claim 1, wherein the determining, based on the incrementing the activity metric comprises determining a ratio between a first counter incremented based on a receipt of a first transmission control packet and a second counter incremented based on a receipt of a second transmission control packet.

9. The method of claim 1, further comprising determining a response to the anomalous activity based on a determination that the activity metric exceeds the threshold.

10. The method of claim 1 comprising:

hashing the one or more fields of the packet to determine the classification, wherein the classification is associated with a flow of one or more packets comprising similar values in the one or more fields; and

incrementing, based on the classification, the first counter associated with the flow.

11. A network device associated comprising:

a parser configured to parse a packet into one or more fields;

a classification module configured to determine a classification of the packet based on the one or more fields;

an action table including the classification of the packet and one or more corresponding actions;

a monitor configured to determine when a counter is incremented based on the corresponding actions, wherein the counter is associated with a set of one or more counters;

an activity engine configured to determine, based on the set of one or more counters and including the incremented counter, an activity metric associated with the packet; and

comparison logic configured to determine whether anomalous activity exists on the network based on a comparison of the activity metric to a threshold associated with the anomalous activity.

12. The network device of claim 11, wherein the parser is configured to receive the packet.

13. The network device of claim 11, wherein the classification module is configured to compare the one or more fields to one or more rules associated with classifying the packet.

14. The network device of claim 13, wherein the one or more rules correspond to one or more of the actions.

15. The network device of claim 11, wherein the action table comprises one or more rules associated with the classification of the packet and the one or more corresponding actions.

16. The network device of claim 11, wherein the monitor is configured to determine which of a plurality of counters is included in the set of one or more counters.

17. The network device of claim 11, wherein the activity engine is configured to:

retrieve values from each of the set one or more counters associated with the incremented counter, including the incremented counter; and

compute the activity metric based on the retrieved values.

18. The network device of claim 11, further comprising a response module configured to determine a response to the anomalous activity based on the comparison of the activity metric to the threshold.

19. A computer program product for detecting anomalous activity on a network, the computer program product being tangibly embodied on a computer-readable medium configured to cause a data processing apparatus to detect the anomalous activity on the network, the computer program product configured to:

determine a classification of a packet received from the network based on one or more classification rules associated with the classification;

determine one or more actions to be performed based on the classification, the one or more actions including incrementing a first counter of a plurality of counters associated with detecting the anomalous activity;

determine an activity metric based on the plurality of counters, wherein the activity metric is anticipated to fall within a threshold; and

determine a response to the anomalous activity based upon a determination that the activity metric falls beyond the threshold.

20. The computer program product of claim 19, wherein the computer program product is configured to determine the response to the anomalous activity, wherein the response is anticipated to offset at least a portion of the anomalous activity.