Method for sharing cybersecurity threat analysis and defensive measures amongst a community

1. A method for a cyber threat defense system, comprising:

comparing input data monitoring a network entity to at least one machine-learning model trained on a normal benign behavior of the network entity using a normal behavior benchmark describing parameters corresponding to a normal pattern of activity for that network entity to spot behavior on the network deviating from a normal benign behavior of that network entity, where the network entity is at least one of a user and a device associated with a network;

identifying whether the network entity is in a breach state of the normal behavior benchmark;

identifying whether the breach state and a chain of relevant behavioral parameters deviating from the normal benign behavior of that network entity correspond to a cyber threat;

generating an inoculation pattern using the cyber threat as a template by describing the breach state and the chain of relevant behavioral parameters corresponding to the cyber threat identified by the cyber threat module;

anonymizing the inoculation pattern to remove any personally identifiable information for the network entity from the inoculation pattern;

sending an inoculation notice having the inoculation pattern to a target device to warn of a potential cyber threat; and

causing one or more autonomous actions to be taken to contain the cyber threat when a threat risk parameter from the cyber threat module is equal to or above an actionable threshold with an autonomous response module, rather than a human taking an action, causing the one or more autonomous actions to be taken to contain the cyber threat, initiating the one or more autonomous actions to counteract the behavior on the network deviating from the normal benign behavior of that network entity, leaving the normal behavior unaffected.

2. The method for the cyber threat defense system of claim 1 , further comprising:

reviewing any suggested response actions detailed in an inoculation package with the autonomous response module and select a most appropriate actions to take in a current situation.

3. The method for the cyber threat defense system of claim 1 , further comprising:

starting the one or more autonomous responses with a minimum level of disruption, aiming to surgically stop an attack by the cyber threat without affecting normal organizational activity; and

adapting the one or more autonomous responses when the attack by the cyber threat at least one of i) changes in nature and ii) becomes more aggressive, then the autonomous response module will select more severe controls to apply to an infected device or devices.

4. The method for the cyber threat defense system of claim 1 , further comprising:

creating and sharing an inoculation package containing one or more digital antibodies for previously unknown cyber threats with the one or more autonomous responses to take in response to an attack by the cyber threat; and

using an intelligence model to determine what an appropriate autonomous response in the one or more autonomous responses should be to the previously unknown threat to defend the network against the previously unknown cyber threat.

5. The method for the cyber threat defense system of claim 1 , further comprising:

receiving, from a user analyst, at least one of a triggering input directing transmission of the inoculation notice to the target device and a blocking input preventing transmission of the inoculation notice to the target device.

6. The method for the cyber threat defense system of claim 1 , further comprising:

generating a threat risk parameter listing a set of values describing aspects of the cyber threat.

7. The method for the cyber threat defense system of claim 6 , further comprising:

populating the threat risk parameter with at least one of a confidence score indicating a threat likelihood describing a probability that the breach state is the cyber threat, a severity score indicating a percentage that the network entity in the breach state is deviating from the at least one model, and a consequence score indicating a severity of damage attributable to the cyber threat.

8. The method for the cyber threat defense system of claim 6 , further comprising:

comparing the threat risk parameter to a benchmark matrix having a set of benchmark scores to determine whether to send the inoculation notice.

9. The method for the cyber threat defense system of claim 8 , further comprising:

assigning a weight to each benchmark score to assign a relative importance to each benchmark score.

10. A non-transitory computer readable medium comprising computer readable code operable, when executed by one or more processing apparatuses in the cyber threat defense system to instruct a computing device to perform the method of claim 1 .

11. A cyber-threat coordinator-component, comprising:

a comparison module implemented in logic and configured to execute a comparison of the input data input data monitoring a network entity to at least one machine-learning model trained on a normal benign behavior of the network entity using a normal behavior benchmark describing parameters corresponding to a normal pattern of activity for that network entity to spot behavior on the network deviating from a normal benign behavior of that network entity to identify whether the network entity is in a breach state of the normal behavior benchmark, where the network entity representing at least one of a user and a device associated with a network;

a cyber threat module implemented in logic and configured to identify whether the breach state identified by the comparison module and a chain of relevant behavioral parameters deviating from the normal benign behavior of that network entity correspond to a cyber threat;

an inoculation module implemented in logic and configured to generate an inoculation pattern using the cyber threat as a template by describing the breach state and the chain of relevant behavioral parameters corresponding to the cyber threat identified by the cyber threat module, to anonymize the inoculation pattern to remove any personally identifiable information for the network entity from the inoculation pattern, and to store the inoculation pattern in an inoculation record in a network-accessible inoculation database; and

an autonomous response module implemented in logic and configured to cooperate with the inoculation module, where the autonomous response module, rather than a human taking an action, is configured to cause one or more autonomous actions to be taken to contain the cyber threat when a threat risk parameter from the cyber threat module is equal to or above an actionable threshold, where the cyber threat module is configured to cooperate with the autonomous response module to cause the one or more autonomous actions to be taken to contain the cyber threat, where the autonomous response module is configured to initiate the one or more autonomous actions to counteract the behavior on the network deviating from the normal benign behavior of that network entity, leaving the normal behavior unaffected.

12. The apparatus for the cyber threat defense system of claim 11 , wherein the inoculation module is configured to generate an inoculation notice having the inoculation pattern to a target device to warn of a potential breach state of normal behavior corresponding to that cyber threat by the target device and to send the inoculation notice to a target device related to the network entity; and

where the autonomous response module is configured to review any suggested response actions detailed in an inoculation package and select a most appropriate actions to take in a current situation.

13. The apparatus for the cyber threat defense system of claim 11 , wherein the inoculation module is configured to create an entity cluster to group the network entity with other entities of the network based on the chain of relevant behavior parameters of the inoculation pattern.

14. The apparatus for the cyber threat defense system of claim 13 , wherein the inoculation module is configured to select a target device for notification regarding the inoculation pattern based on the entity cluster.

15. The apparatus for the cyber threat defense system of claim 11 , wherein the autonomous response module is configured to start the one or more autonomous responses with a minimum level of disruption, aiming to surgically stop an attack by the cyber threat without affecting normal organizational activity.

16. The apparatus for the cyber threat defense system of claim 11 , wherein the autonomous response module is configured to adapt the one or more autonomous responses when an attack by the cyber threat at least one of i) changes in nature and ii) becomes more aggressive, then the autonomous response module will select more severe controls to apply to an infected device or devices.

17. The apparatus for the cyber threat defense system of claim 11 , wherein the inoculation module is configured to update the inoculation pattern in the inoculation record based on a subsequent event.

18. The apparatus for the cyber threat defense system of claim 11 , wherein the inoculation module is configured cooperate with the autonomous response module to create and share an inoculation package containing one or more digital antibodies for previously unknown cyber threats with the one or more autonomous responses to take in response to an attack by the cyber threat.

19. The apparatus for the cyber threat defense system of claim 11 , wherein an intelligence model is used by the autonomous response module to determine what an appropriate autonomous response in the one or more autonomous responses should be to a previously unknown threat to defend the network against the previously unknown cyber threat.

20. A network, comprising:

at least one firewall;

at least one network switch;

multiple computing devices operable by users of the network;

a cyber-threat coordinator-component that includes

a comparison module implemented in logic and configured to execute a comparison of the input data monitoring a network entity to at least one machine-learning model trained on a normal benign behavior of the network entity using a normal behavior benchmark describing parameters corresponding to a normal pattern of activity for that network entity to spot behavior on the network deviating from a normal benign behavior of that network entity to identify whether the network entity is in a breach state of the normal behavior benchmark, where the network entity is at least one of a user and a device associated with a network;

a cyber threat module implemented in logic and configured to identify whether the breach state identified by the comparison module and a chain of relevant behavioral parameters deviating from the normal benign behavior of that network entity correspond to a cyber threat;

an inoculation module implemented in logic and configured to generate an inoculation pattern using the cyber threat as a template by describing the breach state and the chain of relevant behavioral parameters corresponding to the cyber threat identified by the cyber threat module, to anonymize the inoculation pattern to remove any personally identifiable information for the network entity from the inoculation pattern, and to send an inoculation notice having the inoculation pattern to a target device, and

at least one output port to send the inoculation notice to a target device;

an autonomous response module implemented in logic and configured to cooperate with the inoculation module, where the autonomous response module, rather than a human taking an action, is configured to cause one or more autonomous actions to be taken to contain the cyber threat when a threat risk parameter from the cyber threat module is equal to or above an actionable threshold, where the cyber threat module is configured to cooperate with the autonomous response module to cause the one or more autonomous actions to be taken to contain the cyber threat, where the autonomous response module is configured to initiate the one or more autonomous actions to counteract the behavior on the network deviating from the normal benign behavior of that network entity, leaving the normal behavior unaffected; and

wherein the cyber-threat coordinator-component leverages an improvement in the network entity to improve performance by the target device by containing the detected threat and minimizing an amount of CPU cycles, memory space, and power consumed by that detected threat in the network entity when the detected threat is contained by the initiated actions.