Enhancing hybrid traditional neural networks with liquid neural network units for cyber security and offense protection
Aspects of the disclosure relate to enhancing hybrid traditional neural networks with liquid neural networks for cyber security and offense protection. A computing platform may receive a request to access enterprise organization data. The computing platform may compare the current request to previous requests to determine whether a similar request was previously processed. If a similar request was not previously processed, the computing platform may flag the request as a threat and may analyze the request. The computing platform may extract data from the request and may use the extracted data to generate rules, threat detection algorithms, and training models. The computing platform may use the rules, threat detection algorithms, and training models to train a deep learning neural network to identify and handle threats to an enterprise organization.
1. A method comprising:
by a computing device including one or more processors and memory:
determining, by a liquid neural network unit, that a consumer request to access data is a threat to an enterprise organization;
generating, by a readout and prediction unit, rules and algorithms to handle the threat;
encrypting, by a rules and training unit, the rules and the algorithms;
transmitting, from the rules and training unit and to a deep learning neural network, the encrypted rules and the encrypted algorithms;
training the deep learning neural network to handle the threat using the encrypted rules and the encrypted algorithms;
duplicating, by the deep learning neural network and in a black box computing environment, the threat; and
training the deep learning neural network to detect and handle threat techniques used in the duplicated threat.
2. The method of claim 1 , wherein the determining further comprises:
extracting data from the consumer request;
comparing the extracted data to previous consumer requests;
determining whether the extracted data is similar to at least one previous consumer request; and
transmitting a notification, wherein the notification indicates one of:
a negative threat detection, based on the extracted data matching the at least one previous consumer request; or
a positive threat detection, based on the extracted data failing to match the at least one previous consumer request.
3. The method of claim 2 , wherein the generating further comprises:
identifying, from the extracted data, information indicating at least one of:
an origin of the consumer request;
a segment of the data targeted by the consumer request; or
authorization credentials to access the data; and
determining, using the information, a strategy to protect the data.
4. The method of claim 1 , wherein the rules and the algorithms comprise at least one of:
a training model; or
a threat detection algorithm.
5. The method of claim 4 , wherein the training model comprises instructions for:
storing, by the deep learning neural network, the rules and the algorithms generated by the readout and prediction unit;
accessing, by the deep learning neural network, the rules and the algorithms upon receipt of a second consumer request; and
implementing, by the deep learning neural network, the rules and the algorithms to handle the second consumer request.
6. The method of claim 5 , wherein the threat detection algorithm comprises instructions for comparing, by the deep learning neural network, the second consumer request to previous consumer requests to determine whether the second consumer request is a second threat.
7. The method of claim 1 , wherein the duplicating further comprises:
deconstructing the threat into discrete components;
duplicating each discrete component; and
reconstructing the discrete components into a duplicate threat.
8. A computing platform comprising:
at least one processor;
a communication interface communicatively coupled to the at least one processor; and
memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:
determine, by a liquid neural network unit, that a consumer request to access data is a threat to an enterprise organization;
generate, by a readout and prediction unit, rules and algorithms to handle the threat;
encrypt, by a rules and training unit, the rules and the algorithms;
transmit, from the rules and training unit and to a deep learning neural network, the encrypted rules and the encrypted algorithms;
train the deep learning neural network to handle the threat using the encrypted rules and the encrypted algorithms;
duplicate, by the deep learning neural network and in a black box computing environment, the threat; and
train the deep learning neural network to detect and handle threat techniques used in the duplicated threat.
9. The computing platform of claim 8 , wherein the determining further causes the computing platform to:
extract data from the consumer request;
compare the extracted data to previous consumer requests;
determine whether the extracted data is similar to at least one previous consumer requests; and
transmit a notification, wherein the notification indicates one of:
a negative threat detection, based on the extracted data matching the at least one previous consumer request; or
a positive threat detection, based on the extracted data failing to match the at least one previous consumer request.
10. The computing platform of claim 9 , wherein the generating further causes the computing platform to:
identify, from the extracted data, information indicating at least one of:
an origin of the consumer request;
a segment of the data targeted by the consumer request; or
authorization credentials to access the data; and
determine, using the information, a strategy to protect the data.
11. The computing platform of claim 8 , wherein the rules and the algorithms comprise at least one of:
a training model; or
a threat detection algorithm.
12. The computing platform of claim 11 , wherein the training model further causes the computing platform to generate instructions for:
storing, by the deep learning neural network, the rules and the algorithms generated by the readout and prediction unit;
accessing, by the deep learning neural network, the rules and the algorithms upon receipt of a second consumer request; and
implementing, by the deep learning neural network, the rules and the algorithms to handle the second consumer request.
13. The computing platform of claim 8 , wherein the duplicating causes the computing platform to generate instructions for:
deconstructing the threat into discrete components;
duplicating each discrete component; and
reconstructing the discrete components into a duplicate threat.
14. One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, memory, and a communication interface, cause the computing platform to:
determine, by a liquid neural network unit, that a consumer request to access data is a threat to an enterprise organization;
generate, by a readout and prediction unit, rules and algorithms to handle the threat;
encrypt, by a rules and training unit, the rules and the algorithms;
transmit, from the rules and training unit and to a deep learning neural network, the encrypted rules and the encrypted algorithms;
train the deep learning neural network to handle the threat using the encrypted rules and the encrypted algorithms;
duplicate, by the deep learning neural network and in a black box computing environment, the threat; and
train the deep learning neural network to detect and handle threat techniques used in the duplicated threat.
15. The one or more non-transitory computer-readable media of claim 14 , wherein the determining further causes the computing platform to:
extract data from the consumer request;
compare the extracted data to previous consumer requests;
determine whether the extracted data is similar to at least one previous consumer requests; and
transmit a notification, wherein the notification indicates one of:
a negative threat detection, based on the extracted data matching the at least one previous consumer request; or
a positive threat detection, based on the extracted data failing to match the at least one previous consumer request.
16. The computing platform of claim 15 , wherein the generating further causes the computing platform to:
identify, from the extracted data, information indicating at least one of:
an origin of the consumer request;
a segment of the data targeted by the consumer request; or
authorization credentials to access the data; and
determine, using the information, a strategy to protect the data.
17. The computing platform of claim 14 , wherein the rules and the algorithms comprise at least one of:
a training model; or
a threat detection algorithm.
18. The computing platform of claim 17 , wherein the training model further causes the computing platform to generate instructions for:
storing, by the deep learning neural network, the rules and the algorithms generated by the readout and prediction unit;
accessing, by the deep learning neural network, the rules and the algorithms upon receipt of a second consumer request; and
implementing, by the deep learning neural network, the rules and the algorithms to handle the second consumer request.
19. The computing platform of claim 18 , wherein the threat detection algorithm further causes the computing platform to generate instructions for comparing, by the deep learning neural network, the second consumer request to previous consumer requests to determine whether the second consumer request is a second threat.
20. The computing platform of claim 14 , wherein the duplicating causes the computing platform to generate instructions for:
deconstructing the threat into discrete components;
duplicating each discrete component; and
reconstructing the discrete components into a duplicate threat.