Online transactional behavior through decoy blockchain and smartdust sensing paired with DCNN

1 . A method for a secure online transaction, comprising:

receiving, by a computing platform having one or more processors, memory and a communication interface, transactional information from a user, wherein the transactional information is associated with an ongoing transaction;

receiving, by a first sensor of a plurality of sensors in a sensor network, the transactional information, wherein the plurality of sensors includes a plurality of nanoelectromechanical system sensors;

distributing, by the one or more processors and from the first sensor, the transactional information over the sensor network to additional sensors of the plurality of sensors;

generating, by the one or more processors, a decoy transactional block that imitates the transactional information within a blockchain network of the computing platform, wherein the decoy transactional block is visible to an unauthorized user and the plurality of sensors are not visible to the unauthorized user; and

displaying, by the one or more processors, an association page for the user to enter a verification code, and deleting, by the one or more processors, the decoy transactional block from the blockchain network based on determining that the transactional information is authentic.

2 . The method of claim 1 , wherein the distributing, by the one or more processors and from the first sensor, the transactional information over the sensor network further includes:

initiating, by the one or more processors, a communication among the first sensor and the additional sensors of the plurality of sensors in the sensor network.

3 . The method of claim 2 , wherein the transactional information comprises at least one of:

a credit card or a debit card of the user;

a card verification value (CVV) for the credit card or the debit card;

a name of the user;

a date and time of a transaction;

a location of the transaction;

an internet protocol (IP) address of a device used by the user for the transaction;

an amount of the transaction;

a frequency of use of the credit card or the debit card;

a voltage of computation power that is utilized by the device for the user to enter the transactional information; and

a current and power consumption that is utilized by the device for the user to enter the transactional information.

4 . The method of claim 1 , wherein the determining that the transactional information is authentic comprises:

obtaining, by the one or more processors, historical transactional data of the user from a bank network that matches the transactional information; and

infusing, by the one or more processors, the transactional information and the historical transactional data of the user with an algorithm to determine whether the transactional information is authentic.

5 . The method of claim 4 , wherein the algorithm is a deep convolutional neural network (DCNN) algorithm.

6 . The method of claim 1 , wherein the generating, by the one or more processors, the decoy transactional block that imitates the transactional information within the blockchain network of the computing platform comprises:

generating, by the one or more processors, a matrix of validation nodes within the blockchain network of the computing platform that are filled with decoy transactional values.

7 . A computing platform, comprising:

a plurality of sensors in a sensor network;

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 at least one processor to:

receive transactional information from a user, wherein the transactional information is associated with an ongoing transaction;

wherein a first sensor of the plurality of sensors in the sensor network is configured to:

receive the transactional information, wherein the plurality of sensors includes a plurality of nanoelectromechanical system sensors;

distribute the transactional information over the sensor network to additional sensors of the plurality of sensors;

wherein the computer-readable instructions that, when executed by the at least one processor, cause the at least one processor to further perform:

generate a decoy transactional block that imitates the transactional information within a blockchain network of the computing platform, wherein the decoy transactional block is visible to an unauthorized user and the plurality of sensors are not visible to the unauthorized user; and

display an association page for the user to enter a verification code and delete the decoy transactional block from the blockchain network based on determining that the transactional information is authentic.

8 . The computing platform of claim 7 , wherein the computer-readable instructions that, when executed by the at least one processor, further cause the at least one processor to:

initiate a communication among the first sensor and the additional sensors of the plurality of sensors in the sensor network.

9 . The computing platform of claim 8 , wherein the transactional information comprises at least one of:

a credit card or a debit card of the user;

a card verification value (CVV) for the credit card or the debit card;

a name of the user;

a date and time of a transaction;

a location of the transaction;

an internet protocol (IP) address of a device used by the user for the transaction;

an amount of the transaction;

a frequency of use of the credit card or the debit card;

a voltage of computation power that is utilized by the device for the user to enter the transactional information; and

a current and power consumption that is utilized by the device for the user to enter the transactional information.

10 . The computing platform of claim 7 , wherein the computer-readable instructions that, when executed by the at least one processor, further cause the at least one processor to:

obtain historical transactional data of the user from a bank network that matches the transactional information; and

infuse the transactional information and the historical transactional data of the user with an algorithm to determine whether the transactional information is authentic.

11 . The computing platform of claim 10 , wherein the algorithm is a deep convolutional neural network (DCNN) algorithm.

12 . The computing platform of claim 7 , wherein the computer-readable instructions that, when executed by the at least one processor, further cause the at least one processor to:

generate a matrix of validation nodes within the blockchain network of the computing platform that are filled with decoy transactional values.

13 . A non-transitory computer-readable medium, having computer-executable instructions stored thereon, the computer-executable instructions, when executed by one or more processors of a computing platform, cause the one or more processors to facilitate:

receiving transactional information from a user, wherein the transactional information is associated with an ongoing transaction, wherein the computing platform further comprises a plurality of sensors in a sensor network;

receiving, via a first sensor of the plurality of sensors, the transactional information, wherein the plurality of sensors includes a plurality of nanoelectromechanical system sensors;

distributing, via the first sensor, the transactional information over the sensor to additional sensors of the plurality of sensors;

generating a decoy transactional block that imitates the transactional information within a blockchain network of the computing platform, wherein the decoy transactional block is visible to an unauthorized user and the plurality of sensors are not visible to the unauthorized user; and

displaying an association page for the user to enter a verification code and deleting the decoy transactional block from the blockchain network based on determining that the transactional information is authentic.

14 . The non-transitory computer-readable medium of claim 13 , wherein the computer-executable instructions that, when executed by the one or more processors, further cause the one or more processors to facilitate:

initiating a communication among the first sensor and the additional sensors of the plurality of sensors in the sensor network.

15 . The non-transitory computer-readable medium of claim 13 , wherein the computer-executable instructions that, when executed by the one or more processors, further cause the one or more processors to facilitate:

obtaining historical transactional data of the user from a bank network that matches the transactional information; and

infusing the transactional information and the historical transactional data of the user with an algorithm to determine whether the transactional information is authentic.

16 . The non-transitory computer-readable medium of claim 15 , wherein the algorithm is a deep convolutional neural network (DCNN) algorithm.

17 . The non-transitory computer-readable medium of claim 13 , wherein the computer-executable instructions that, when executed by the one or more processors, further cause the one or more processors to facilitate:

generating a matrix of validation nodes within the blockchain network of the computing platform that are filled with decoy transactional values.