Distributed learning in a computer network
First Claim
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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.
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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.
60 Citations
17 Claims
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1. A method comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. An apparatus, comprising:
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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. - View Dependent Claims (12, 13, 14, 15)
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16. A tangible, non-transitory, computer-readable media having software encoded thereon, the software when executed by a processor operable to:
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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. - View Dependent Claims (17)
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Specification