Distributed learning in a computer network

Patent metadata

Field	Value
Publication number	US-9870537-B2
Application number	US-201414164446-A
Country	US
Kind code	B2
Filing date	Jan 27, 2014
Priority date	Jan 6, 2014
Publication date	Jan 16, 2018
Grant date	Jan 16, 2018

Abstract

In one embodiment, a first data set is received by a network device that is indicative of the statuses of a plurality of network devices when a type of network attack is not present. A second data set is also received that is indicative of the statuses of the plurality of network devices when the type of network attack is present. At least one of the plurality simulates the type of network attack by operating as an attacking node. A machine learning model is trained using the first and second data set to identify the type of network attack. A real network attack is then identified using the trained machine learning model.

First claim

What is claimed is:

1. A method comprising:
sending, by a network device, a request to a network policy engine to initiate collection of a first or a second data set from a plurality of network devices, the first data set indicative of the statuses of the plurality of network devices when a type of network attack is not present and the second data set indicative of the statuses of the plurality of network devices when the type of network attack is present;
receiving, at the network device, an authorization from the network policy engine to begin collection of the first or second data set, the authorization based on an evaluation of an impact of collecting the first or second data sets on network traffic;
in response to receiving the authorization from the network policy engine,
receiving, at the network device, the first data set indicative of the statuses of the plurality of network devices when the type of network attack is not present;
selecting, by the network device, at least one of the plurality of network devices to simulate the type of network attack by operating as an attacking node; and
receiving, at the network device, the second data set indicative of the statuses of the plurality of network devices when the type of network attack is present based on the at least one of the plurality of network devices selected to simulate the type of network attack by operating as an attacking node;

training a machine learning model using the first and second data set to identify the type of network attack; and
identifying a real network attack using the trained machine learning model.

2. The method as in claim 1 , wherein the machine learning model is an artificial neural network (ANN).

3. The method as in claim 1 , further comprising:
requesting the first or second data set from the plurality of network devices, in response to receiving the authorization to begin collection of the first or second data set.

4. The method as in claim 1 , wherein the authorization comprises a scheduled start time for the collection of a simulated attack type, wherein the first or second data set is requested at the start time.

5. The method as in claim 1 , wherein the request to initiate collection of the first or second data set comprises data selected from the group comprising: an estimated time duration for the data collection and an estimated size of the first or second data set.

6. The method as in claim 1 , wherein the authorization comprises an instruction to reduce an estimated duration for the collection of the first or second data set.

7. The method as in claim 1 , further comprising:
sending an instruction to the at least one of the plurality of network devices to simulate the type of network attack.

8. The method as in claim 1 , wherein the at least one of the plurality of network devices that simulates the type of network attack is selected randomly.

9. The method as in claim 1 , further comprising:
notifying the network policy engine that the machine learning model has been trained.

10. The method as in claim 1 , further comprising:
notifying a network policy engine of the network attack identified using the machine learning model.

11. An apparatus, comprising:
one or more network interfaces to communicate in a computer network;
a processor coupled to the network interfaces and configured to execute one or more processes; and
a memory configured to store a process executable by the processor, the process when executed operable to:
send a request to a network policy engine to initiate collection of a first or a second data set from a plurality of network devices, the first data set indicative of the statuses of the plurality of network devices when a type of network attack is not present and the second data set indicative of the statuses of the plurality of network devices when the type of network attack is present;
receive an authorization from the network policy engine to begin collection of the first or second data set, the authorization based on an evaluation of an impact of collecting the first or second data set on network traffic;
in response to receiving the authorization from the network policy engine,
receive the first data set indicative of the statuses of the plurality of network devices when the type of network attack is not present;
select at least one of the plurality of network devices to simulate the type of network attack by operating as an attacking node; and
receive the second data set indicative of the statuses of the plurality of network devices when the type of network attack is present based on the at least one of the plurality of network devices selected to simulate the type of network attack by operating as an attacking node;

train a machine learning model using the first and second data set to identify the type of network attack; and
identify a real network attack using the trained machine learning model.

12. The apparatus as in claim 11 , wherein the machine learning model is an artificial neural network (ANN).

13. The apparatus as in claim 11 , wherein the process when executed is further operable to:
send an instruction to the at least one of the plurality of network devices to simulate the type of network attack.

14. The apparatus as in claim 11 , wherein the process when executed is further operable to:
request the first or second data set from the plurality of network devices, in response to receiving the authorization to begin collection of the first or second data set.

15. The apparatus as in claim 11 , wherein the authorization comprises a scheduled start time for the collection, and wherein the first or second data set is requested at the start time.

16. A tangible, non-transitory, computer-readable media having software encoded thereon, the software when executed by a processor operable to:
send a request to a network policy engine to initiate collection of a first or a second data set from a plurality of network devices, the first data set indicative of the statuses of the plurality of network devices when a type of network attack is not present and the second data set indicative of the statuses of the plurality of network devices when the type of network attack is present;
receive an authorization from the network policy engine to begin collection of the first or second data set, the authorization based on an evaluation of an impact of collecting the first or second data sets on network traffic;
in response to receiving the authorization from the network policy engine,
receive the first data set indicative of the statuses of the plurality of network devices when the type of network attack is not present;
select at least one of the plurality of network devices to simulate the type of network attack by operating as an attacking node;
receive the second data set indicative of the statuses of the plurality of network devices when the type of network attack is present based on the at least one of the plurality of network devices selected to simulate the type of network attack by operating as an attacking node;

train a machine learning model using the first and second data set to identify the type of network attack; and
identify a real network attack using the trained machine learning model.

17. The computer-readable media as in claim 16 , wherein the machine learning model is an artificial neural network (ANN).

Assignees

• Cisco Tech Inc

Inventors

• Vasseur Jean-Philippe
• Cruz Mota Javier
• Di Pietro Andrea

Classifications

• G06N3/045

  Combinations of networks · CPC title

• G06N3/098

  Distributed learning, e.g. federated learning · CPC title

• G06N3/0499

  Feedforward networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• H04L41/142

  using statistical or mathematical methods · CPC title