Determining a reputation of a network entity

• US 10,623,284 B2
• Filed: 05/06/2019
• Issued: 04/14/2020
• Est. Priority Date: 06/05/2015
• Status: Active Grant

• Current Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Original Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Inventors: Yadav, Navindra, Rao, Supreeth Hosur Nagesh, Kulshreshtha, Ashutosh, Madani, Omid, Pang, Jackson Ngoc Ki, Deen, Khawar, Scheib, Ellen Christine
• Primary Examiner(s): Hirl, Joseph P
• Assistant Examiner(s): Saadoun, Hassan
• Application Number:
  US16/403,797
• Publication Number:
  US 20190260653A1
• Time in Patent Office: 344 Days
• Field of Search: None
• CPC Class Codes: G06F 16/122 using management policies backup systems G06F11/1446; file migration policies for HSM systems G06F16/185
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• CPC Class Codes: H04L 63/06 : for supporting key management in a packet data network cryptographic mechanisms or cryptographic arrangements for key management H04L9/08
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• CPC Class Codes: H04L 63/1408 : by monitoring network traffic monitoring network traffic per se H04L43/00
• CPC Class Codes: H04L 63/1416 : Event detection, e.g. attack signature detection
• CPC Class Codes: H04L 63/1425 : Traffic logging, e.g. anomaly detection
• CPC Class Codes: H04L 63/1433 : Vulnerability analysis
• CPC Class Codes: H04L 63/1441 : Countermeasures against malicious traffic countermeasures against attacks on cryptographic mechanisms H04L9/002
• CPC Class Codes: H04L 63/145 : the attack involving the propagation of malware through the network, e.g. viruses, trojans or worms
• CPC Class Codes: H04L 63/1458 : Denial of Service
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• CPC Class Codes: H04W 84/18 : Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

An example method can include monitoring a network to identify flows between nodes in the network. Once flows have been identified, the flows can be tagged and labelled according to the type of traffic they represent. If a flow represents malicious or otherwise undesirable traffic, it can be tagged accordingly. A request can then be made for a reputation score of an entity which can identify one or more nodes of the network.

Claims

1. A computer-implemented method, comprising:

identifying a type of traffic for one or more flows associated with one or more nodes;

determining a reputation score of one or more nodes based on the type of traffic through the one or more nodes; and

blocking a node of the one or more nodes from sending traffic in response to the reputation score for that node being below a predetermined threshold;

wherein the reputation score is calculated by;

Reputation Score=Σ

i=1n Valuei+(Recoveryi*[TimecurrentTimei])

where;

n is the number of events identified during the identifying;

Value_i is an initial relative impact of the type of traffic of an ith event on the reputation score, where Value_i is different for at least two different values of i;

Recovery_i is a constant for the type of traffic of the ith event, wherein the Recovery for at least one event is non-zero, and the recovery for at least one other event is zero;

TiMe_current is a time value based on a current time;

Time_i is a time value based on a time of the ith event.

2. The computer-implemented method of claim 1, further comprising, before the determining, analyzing network traffic data to classify a type of traffic for each flow of a plurality of flows.

3. The computer-implemented method of claim 2, wherein analyzing the network traffic data to classify a type of traffic for each flow includes:

matching the flow to a known pattern of traffic.

4. The computer-implemented method of claim 3, wherein the known pattern of traffic corresponds to at least one of a DDOS attack, a FIN scan, or a port knock.

5. The computer-implemented method of claim 2, wherein analyzing the network traffic data to classify a type of traffic for each flow includes:

identifying one or more network policies associated with the flow; and

determining whether the flow complies with the one or more network policies.

6. The computer-implemented method of claim 1, wherein the reputation score is associated with at least one of an endpoint, an endpoint group, a pair of endpoints, a pair of endpoint groups, an endpoint and an endpoint group, a user name for a process, a process, or a path for a process of a network.

7. The computer-implemented method of claim 1, wherein determining the reputation score includes:

determining at least one of the one or more flows is tagged as malicious or misconfigured; and

decreasing the reputation score.

8. A non-transitory computer readable media storing instructions which when executed by a system cause the system to perform operations comprising:

identifying a type of traffic for one or more flows associated with one or more nodes;

determining a reputation score of one or more nodes based on the type of traffic through the one or more nodes; and

blocking a node of the one or more nodes from sending traffic in response to the reputation score for that node being below a predetermined threshold;

wherein the reputation score is calculated by;

Reputation Score=Σ

i=1n Valuei+(Recoveryi*[Timecurrent−

Timei])

where;

n is the number of events identified during the identifying;

Value_i is an initial relative impact of the type of traffic of an ith event on the reputation score, where Value_i is different for at least two different values of i;

Recovery_i is a constant for the type of traffic of the ith event, wherein the Recovery for at least one event is non-zero, and the recovery for at least one other event is zero;

Time_current is a time value based on a current time;

Time_i is a time value based on a time of the ith event.

9. The media of claim 8, the operations further comprising, before the determining, analyzing network traffic data to classify a type of traffic for each flow of a plurality of flows.

10. The media of claim 9, wherein analyzing network traffic data to classify a type of traffic for each flow includes:

matching the flow to a known pattern of traffic.

11. The media of claim 10, wherein the known pattern of traffic corresponds to at least one of a DDOS attack, a FIN scan, or a port knock.

12. The media of claim 8, wherein analyzing network traffic data to classify a type of traffic for each flow includes:

identifying one or more network policies associated with the flow; and

determining whether the flow complies with the one or more network policies.

13. The media of claim 8, wherein the reputation score is associated with at least one of an endpoint, an endpoint group, a pair of endpoints, a pair of endpoint groups, an endpoint and an endpoint group, a user name for a process, a process, or a path for a process of a network.

14. The media of claim 8, wherein determining the reputation score includes:

determining at least one of the one or more flows is tagged as malicious or misconfigured; and

decreasing the reputation score.

15. A system, comprising:

a non-transitory computer readable media storing instructions;

a processor programmed to execute the instructions to perform operations comprising;

identifying a type of traffic for one or more flows associated with one or more;

determining a reputation score of one or more nodes based on the type of traffic through the one or more nodes; and

blocking a node of the one or more nodes from sending traffic in response to the reputation score for that node being below a predetermined threshold;

wherein the reputation score is calculated by;

Reputation Score=Σ

i=1n Valuei+(Recoveryi*[Timecurrent−

Timei])

where;

n is the number of events identified during the identifying;

Value_i is an initial relative impact of the type of traffic of an ith event on the reputation score, where Value_i is different for at least two different values of i;

Recovery_i is a constant for the type of traffic of the ith event, wherein the Recovery for at least one event is non-zero, and the recovery for at least one other event is zero;

Time_current is a time value based on a current time;

Time_i is a time value based on a time of the ith event.

16. The system of claim 15, the operations further comprising, before the determining, analyzing network traffic data to classify a type of traffic for each flow of a plurality of flows.

17. The system of claim 16, wherein analyzing the network traffic data to classify a type of traffic for each flow includes:

matching the flow to a known pattern of traffic.

18. The system of claim 17, wherein the known pattern of traffic corresponds to at least one of a DDOS attack, a FIN scan, or a port knock.

19. The system of claim 16, wherein analyzing the network traffic data to classify a type of traffic for each flow includes:

identifying one or more network policies associated with the flow; and

determining whether the flow complies with the one or more network policies.

20. The system of claim 15, wherein the reputation score is associated with at least one of an endpoint, an endpoint group, a pair of endpoints, a pair of endpoint groups, an endpoint and an endpoint group, a user name for a process, a process, or a path for a process of a network.