Graphics processing unit optimization

1. A method for optimizing resources privately, comprising:

mapping, by an algorithmic framework processor, a first structure represented by initial graph data to a second structure, the second structure having a lower dimension than the first structure and containing real numbers, thereby generating an initial client embedding data set;

computing, by an information gain processor, an initial information gain corresponding to the initial client embedding data set;

training, by a dedicated server, a machine learning model based on the initial client embedding data set to generate at least one initial attack path in the initial graph data;

mapping, using the algorithmic framework processor, a third structure representing a second graph data to a fourth structure, the fourth structure having a lower dimension than the third structure and containing real numbers, thereby generating a second client embedding data set;

computing, using the information gain processor, a second information gain corresponding to the second client embedding data set;

computing, using a difference processor, a difference between the first information gain and the second information gain; and

retraining, using the dedicated server, the machine learning model if the difference between the first information gain and the second information gain meets a predetermined threshold to generate at least one new attack path in the second graph data

scoring, by a scoring processor the at least one initial attack path, thereby generating at least one scored initial attack path;

ranking, by a ranking processor the at least one scored initial attack path, thereby generating a ranking of initial attack paths;

providing the ranking of new attack paths to a user interface;

scoring, by the scoring processor the at least one new attack path, thereby generating at least one scored new attack path;

ranking, by the ranking processor the at least one scored new attack path, thereby generating a ranking of new attack paths; and

providing the ranking of new attack paths to the user interface.

2. The method according to claim 1 , wherein the information technology associated with the organization includes any one of (i) at least one on-premises resource, (ii) at least one third party service, (iii) at least one cloud computing service, (iv) a plurality of credentials associated with (i), (ii), (iii), or (v) any combination of (i), (ii), (iii) and (vi).

3. The method according to claim 1 , wherein the scoring is performed by:

measuring a utility of vulnerability associated with at least node in the initial attack path.

4. The method according to claim 1 , wherein measuring the utility of vulnerability includes:

obtaining an exploit associated with at least one node in the attack path.

5. The method according to claim 1 , further comprising:

receiving, from a user interface, an update including attribute data related to any one of (i) at least one node represented by the initial graph data, (ii) at least one edge represented by the initial graph data, or (iii) combination of (i) and (ii); and

retraining, using the dedicated server, the machine learning model using the attribute data and the client embedding data set.

6. The method according to claim 5 , wherein the retraining of the machine learning model using the attribute data and the client embedding data set occurs if the difference between the first information gain and the second information gain is less than a predetermined threshold.

7. The method according to claim 5 , further comprising:

calculating, by a scoring processor, a score corresponding to a security risk associated with the update or change to attribute data;

computing, by the information gain processor, an initial information gain corresponding to the initial client embedding data set;

training, using the dedicated server, the machine learning model based on the initial client embedding data set to generate at least one initial attack path in the initial graph data;

mapping, using the algorithmic framework processor, a third structure representing a second graph data to a fourth structure, the fourth structure having a lower dimension than the third structure and containing real numbers, thereby generating a second client embedding data set;

computing, using the information gain processor, a second information gain corresponding to the second client embedding data set;

computing, by the difference processor, a difference between the first information gain and the second information gain; and

retraining, using the dedicated server, the machine learning model if the difference between the first information gain and the second information gain meets a predetermined threshold to generate at least one new attack path in the second graph data.

8. A system for optimizing resources privately, comprising:

a memory;

an algorithmic framework processor configured to map a first structure represented by initial graph data to a second structure, the second structure having a lower dimension than the first structure and containing real numbers, thereby generating an initial client embedding data set;

an information gain processor configured to compute an initial information gain corresponding to the initial client embedding data set;

a dedicated server configured to train a machine learning model based on the initial client embedding data set to generate at least one initial attack path in the initial graph data;

the algorithmic framework processor further configured to map a third structure representing a second graph data to a fourth structure, the fourth structure having a lower dimension than the third structure and containing real numbers, thereby generating a second client embedding data set;

the information gain processor further configured to compute a second information gain corresponding to the second client embedding data set;

a difference processor configured to compute a difference between the first information gain and the second information gain; and

the dedicated server further configured to retrain the machine learning model if the difference between the first information gain and the second information gain meets a predetermined threshold to generate at least one new attack path in the second graph data

a scoring processor configured to score the at least one initial attack path, thereby generating at least one scored initial attack path;

a ranking processor configured to:

rank the at least one scored initial attack path, thereby generating a ranking of initial attack paths, and

provide the ranking of new attack paths to a user interface; the scoring processor further configured to score the at least one new attack path, thereby generating at least one scored new attack path; and

the ranking processor further configured to:

rank the at least one scored new attack path, thereby generating a ranking of new attack paths, and provide the ranking of new attack paths to the user interface.

9. The system according to claim 8 , wherein the information technology associated with the organization includes any one of (i) at least one on-premises resource, (ii) at least one third party service, (iii) at least one cloud computing service, (iv) a plurality of credentials associated with (i), (ii), (iii), or (v) any combination of (i), (ii), (iii) and (vi).

10. The system according to claim 8 , wherein the scoring processor is configured to perform scoring is by measuring a utility of vulnerability associated with at least node in the initial attack path.

11. The system according to claim 8 , wherein measuring the utility of vulnerability includes:

obtaining an exploit associated with at least one node in the attack path.

12. The system according to claim 8 , further comprising:

a user interface configured to receive an update including attribute data related to any one of (i) at least one node represented by the initial graph data, (ii) at least one edge represented by the initial graph data, or (iii) combination of (i) and (ii); and

the dedicated server is further configured to retrain the machine learning model using the attribute data and the client embedding data set.

13. The system according to claim 12 , wherein the retraining of the machine learning model using the attribute data and the client embedding data set occurs if the difference between the first information gain and the second information gain is less than a predetermined threshold.

14. The system according to claim 12 , further comprising:

a scoring processor configured to calculate a score corresponding to a security risk associated with the update or change to attribute data;

the information gain processor further configured to compute an initial information gain corresponding to the initial client embedding data set;

the dedicated server further configured to train the machine learning model based on the initial client embedding data set to generate at least one initial attack path in the initial graph data;

the algorithmic framework processor further configured to map a third structure representing a second graph data to a fourth structure, the fourth structure having a lower dimension than the third structure and containing real numbers, thereby generating a second client embedding data set;

the information gain processor further configured to compute a second information gain corresponding to the second client embedding data set;

the difference processor further configured to compute a difference between the first information gain and the second information gain; and

the dedicated server further configured to retrain the machine learning model if the difference between the first information gain and the second information gain meets a predetermined threshold to generate at least one new attack path in the second graph data.

15. A non-transitory computer-readable medium having stored thereon one or more sequences of instructions for causing one or more processors to perform:

mapping a first structure represented by initial graph data to a second structure, the second structure having a lower dimension than the first structure and containing real numbers, thereby generating an initial client embedding data set;

computing an initial information gain corresponding to the initial client embedding data set;

training a machine learning model based on the initial client embedding data set to generate at least one initial attack path in the initial graph data;

mapping a third structure representing a second graph data to a fourth structure, the fourth structure having a lower dimension than the third structure and containing real numbers, thereby generating a second client embedding data set;

computing a second information gain corresponding to the second client embedding data set;

computing a difference between the first information gain and the second information gain; and

retraining the machine learning model if the difference between the first information gain and the second information gain meets a predetermined threshold to generate at least one new attack path in the second graph data

scoring the at least one initial attack path, thereby generating at least one scored initial attack path;

ranking the at least one scored initial attack path, thereby generating a ranking of initial attack paths;

providing the ranking of new attack paths to a user interface;

scoring the at least one new attack path, thereby generating at least one scored new attack path;

ranking the at least one scored new attack path, thereby generating a ranking of new attack paths; and

provide the ranking of new attack paths to the user interface.

16. The non-transitory computer-readable medium of claim 15 , wherein the information technology associated with the organization includes any one of (i) at least one on-premises resource, (ii) at least one third party service, (iii) at least one cloud computing service, (iv) a plurality of credentials associated with (i), (ii), (iii), or (v) any combination of (i), (ii), (iii) and (vi).

17. The non-transitory computer-readable medium of claim 15 , wherein the scoring is performed by:

measuring a utility of vulnerability associated with at least node in the initial attack path.

18. The non-transitory computer-readable medium of claim 15 , wherein measuring the utility of vulnerability includes:

obtaining an exploit associated with at least one node in the attack path.

19. The non-transitory computer-readable medium of claim 15 , further having stored thereon a sequence of instructions for causing the one or more processors to perform:

calculating a score corresponding to a security risk associated with the update or change to attribute data;

computing an initial information gain corresponding to the initial client embedding data set;

training the machine learning model based on the initial client embedding data set to generate at least one initial attack path in the initial graph data;

mapping a third structure representing a second graph data to a fourth structure, the fourth structure having a lower dimension than the third structure and containing real numbers, thereby generating a second client embedding data set;

computing a second information gain corresponding to the second client embedding data set;

computing a difference between the first information gain and the second information gain; and

retraining the machine learning model if the difference between the first information gain and the second information gain meets a predetermined threshold to generate at least one new attack path in the second graph data.

20. The non-transitory computer-readable medium of claim 15 , further having stored thereon a sequence of instructions for causing the one or more processors to perform:

receiving an update including attribute data related to any one of (i) at least one node represented by the initial graph data, (ii) at least one edge represented by the initial graph data, or (iii) combination of (i) and (ii); and

retraining the machine learning model using the attribute data and the client embedding data set.

21. The non-transitory computer-readable medium of claim 15 , wherein the retraining of the machine learning model using the attribute data and the client embedding data set occurs if the difference between the first information gain and the second information gain is less than a predetermined threshold.