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US 2023/0146962 A1Published application
Automatic retraining of machine learning models to detect ddos attacks
AssigneeCisco Technology, Inc.
InventorsK. Tirumaleswar Reddy, Daniel G. Wing, Blake Harrell Anderson, David McGrew
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Abstract
In one embodiment, a device in a network receives an attack mitigation request regarding traffic in the network. The device causes an assessment of the traffic, in response to the attack mitigation request. The device determines that an attack detector associated with the attack mitigation request incorrectly assessed the traffic, based on the assessment of the traffic. The device causes an update to an attack detection model of the attack detector, in response to determining that the attack detector incorrectly assessed the traffic.
Claims20
A method comprising:
- monitoring, at an attack detector, network traffic to detect a Distributed Denial of Service (DDoS) attack by applying a machine learning-based attack detection model against one or more attributes of the network traffic;
- responsive to detection by the attack detector of a potential DDoS attack, transmitting an attack mitigation message indicating the potential DDoS attack to an attack mitigation server;
- causing, by the attack mitigation server, an assessment of the network traffic in response to the attack mitigation message;
- initiating, based on the assessment of the network traffic, an attack mitigation response;
- determining that the attack detector associated with the attack mitigation message incorrectly assessed the network traffic based on the assessment of the network traffic;
- receiving, at the attack detector, an update message relating to the attack detection model in response to the determination that the attack detector incorrectly assessed the network traffic;
- and
- using, by the attack detector, data in the received update message to update the attack detection model.
5 more elements
A system comprising:
- an attack detector, wherein the attack detector comprises:
- one or more network interfaces to communicate with a network;
- a processor coupled to the one or more network interfaces and configured to execute one or more processes;
- and
- a memory configured to store a process that is executable by the processor, the process when executed causing the attack detector to:
- monitor network traffic to detect a Distributed Denial of Service (DDoS) attack by applying a machine learning-based attack detection model against one or more attributes of the network traffic, and
- responsive to detection by the attack detector of a potential DDoS attack, transmit an attack mitigation message indicating the potential DDoS attack to an attack mitigation server;
- and
- the attack mitigation server, wherein the attack mitigation server comprises:
- one or more network interfaces to communicate with a network;
- a processor coupled to the one or more network interfaces and configured to execute one or more processes;
- and
- a memory configured to store a process that is executable by the processor, the process when executed causing the attack mitigation server to:
- cause an assessment of the network traffic in response to the attack mitigation message,
- initiate, based on the assessment of the network traffic, an attack mitigation response, and
- determine that the attack detector associated with the attack mitigation message incorrectly assessed the network traffic based on the assessment of the network traffic;
- wherein the attack detector is further operable to:
- receive an update message relating to the attack detection model in response to the determination that the attack detector incorrectly assessed the network traffic, and
- use data in the received update message to update the attack detection model.
16 more elements
Dependencies are inferred from the claim text.