Automated identification of security issues
Disclosed are various approaches for automating the detection and identification of security issues. A plurality of signals received from a plurality of security devices are analyzed to identify a predicted security incident, each of the plurality of signals indicating a potential security issue. A confidence score is then calculated for the predicted security incident. At least one compliance policy is then evaluated to determine whether to perform a remedial action specified in the compliance policy, wherein a determination to perform the remedial action is based at least in part on the confidence score. Finally, the remedial action is performed in response to an evaluation of the at least one compliance policy.
1. A system, comprising:
a computing device comprising a processor device and a memory; and
machine-readable instructions stored in the memory that, when executed by the processor device, cause the computing device to at least:
analyze a plurality of signals received from a plurality of security devices to identify a predicted network security incident associated with a network based on a machine learning model identifying a pattern among the plurality of signals that corresponds to a previous network security incident, each of the plurality of signals indicating a potential network security issue, the plurality of security devices generating the plurality of signals based on monitoring network traffic on the network;
calculate a confidence score for the predicted network security incident, the confidence score representing an accuracy of a precision of the predicted network security incident;
evaluate at lease one compliance policy to determine whether to perform a remedial action specified in the at least one compliance policy, wherein a determination to perform the remedial action is based at least in part on the confidence score exceeding a confidence threshold specified by the at least one compliance policy; and
direct the plurality of security devices to perform the remedial action in response to an evaluation of the at least one compliance policy.
2. The system of claim 1 , wherein the machine-readable instructions that cause the computing device to direct the plurality of security devices to perform the remedial action further cause the computing device to at least send a message to a client device associated with an administrative user, the message comprising a summary of the predicted network security incident, the confidence score, and the remedial action.
3. The system of claim 2 , wherein the machine-readable instructions that cause the computing device to direct the plurality of security devices to perform the remedial action further cause the computing device to at least direct the plurality of security devices to perform the remedial action in response to a reply received from the client device associated with the administrative user.
4. The system of claim 2 , wherein the machine-readable instructions that cause the computing device to direct the plurality of security devices to perform the remedial action further cause the computing device to at least direct the plurality of security devices to perform the remedial action in response to a failure to receive a reply from the client device associated with the administrative user within a predefined period of time.
5. The system of claim 1 , wherein the machine-readable instructions that analyze the plurality of signals to identify the predicted network security incident implement a Bayesian network to identify the predicted network security incident.
6. The system of claim 1 , wherein the remedial action specified in the at least one compliance policy indicates that at least one client device is to be blocked from accessing the network.
7. The system of claim 1 , wherein the plurality of signals are stored in a data store accessible to the computing device.
8. A method, comprising:
analyzing a plurality of signals received from a plurality of security devices to identify a predicted network security incident associated with a network based on a machine learning model identifying a pattern among the plurality of signals that corresponds to a previous network security incident, each of the plurality of signals indicating a potential network security issue, the plurality of security devices generating the plurality of signals based on monitoring network traffic on the network;
calculating a confidence score for the predicted network security incident, the confidence score represented an accuracy of a prediction of the predicted network security incident;
evaluating at lease one compliance policy to determine whether to perform a remedial action specified in the at least one compliance policy, wherein a determination to perform the remedial action is based at least in part on the confidence score exceeding a confidence threshold specified by the at least one compliance policy; and
directing the plurality of security devices to perform the remedial action in response to an evaluation of the at least one compliance policy.
9. The method of claim 8 , wherein directing the plurality of security devices to perform the remedial action further comprises sending a message to a client device associated with an administrative user, the message comprising a summary of the predicted network security incident, the confidence score, and the remedial action.
10. The method of claim 9 , wherein directing the plurality of security devices to perform the remedial action occurs in response to a reply received from the client device associated with the administrative user.
11. The method of claim 9 , wherein directing the plurality of security devices to perform the remedial action occurs in response to a failure to receive a reply from the client device associated with the administrative user within a predefined period of time.
12. The method of claim 8 , wherein the predicted network security incident is identified using a Bayesian network.
13. The method of claim 8 , wherein the remedial action specified in the at least one compliance policy indicates that at least one client device is to be blocked from accessing the network.
14. The method of claim 8 , wherein the plurality of signals are stored in a data store.
15. A non-transitory, computer-readable medium comprising machine-readable instructions that, when executed by a processor device, cause a computing device to at least:
analyze a plurality of signals received from a plurality of security devices to identify a predicted network security incident associated with a network based on a machine learning model identifying a pattern among the plurality of signals that corresponds to a previous network security incident, each of the plurality of signals indicating a potential network security issue, the plurality of security devices generating the plurality of signals based on monitoring network traffic on the network;
calculate a confidence score for the predicted network security incident, the confidence score represented an accuracy of a prediction of the predicted network security incident;
evaluate at lease one compliance policy to determine whether to perform a remedial action specified in the at least one compliance policy, wherein a determination to perform the remedial action is based at least in part on the confidence score exceeding a confidence threshold specified by the at least one compliance policy; and
direct the plurality of security devices to perform the remedial action in response to an evaluation of the at least one compliance policy.
16. The non-transitory, computer-readable medium of claim 15 , wherein the machine-readable instructions that cause the computing device to direct the plurality of security devices to perform the remedial action further cause the computing device to at least send a message to a client device associated with an administrative user, the message comprising a summary of the predicted network security incident, the confidence score, and the remedial action.
17. The non-transitory, computer-readable medium of claim 16 , wherein the machine-readable instructions that cause the computing device to direct the plurality of security devices to perform the remedial action further cause the computing device to at least direct the plurality of security devices to perform the remedial action in response to a reply received from the client device associated with the administrative user.
18. The non-transitory, computer-readable medium of claim 16 , wherein the machine-readable instructions that cause the computing device to direct the plurality of security devices to perform the remedial action further cause the computing device to at least direct the plurality of security devices to perform the remedial action in response to a failure to receive a reply from the client device associated with the administrative user within a predefined period of time.
19. The non-transitory, computer-readable medium of claim 15 , wherein the machine-readable instructions that analyze the plurality of signals to identify the predicted network security incident implement a Bayesian network to identify the predicted network security incident.
20. The non-transitory, computer-readable medium of claim 15 , wherein the plurality of signals are stored in a data store accessible to the computing device.