Systems and methods for analyzing virtual data, predicting future events, and improving computer security
The following relates generally to computer security, and more particularly relates to computer security in a virtual environment, such as a metaverse. In some embodiments, one or more processors: receive a set of known events (e.g., security threats) including event classifications; receive data of layers of the virtual environment; detect events in the data of the layers of the virtual environment; and determine correlations between the events in the data of the layers of the virtual environment. The correlations may be between events in different layers of the virtual environment. The one or more processors may also predict future events by analyzing the detected events.
1. A computer-implemented method for improving security in a virtual environment by predicting a future event in the virtual environment, the method comprising:
receiving, via one or more processors, a set of known events including event classifications;
receiving, via the or more processors, data of layers of the virtual environment, wherein the virtual environment enables a user to interact with a virtual world;
detecting, via the one or more processors, events in the data of the layers of the virtual environment;
classifying, via the one or more processors, the detected events into the event classifications; and
predicting, via the one or more processors, the future event by analyzing the detected events by routing the detected events to a trained event machine learning algorithm to predict the future event, wherein the event machine learning algorithm is trained based on data of historical events including layers that the historical events occurred in.
2. The computer-implemented method of claim 1 , wherein the predicted future event corresponds to a classification included in the set of known events.
3. The computer-implemented method of claim 1 , wherein the data of historical events further includes: (i) classifications of the historical events, and (ii) times the historical events occurred.
4. The computer-implemented method of claim 1 , further comprising:
analyzing, via the one or more processors, the detected events to determine a new event classification; and
wherein the predicted future event corresponds to the new event classification.
5. The computer-implemented method of claim 1 , wherein the event classifications in the set of known events include: a character movement classification, a click pattern classification, a message pattern classification, an overlay classification, disorientation classification, an attack on an NFT database classification, a financial fraud classification, a privacy data violation classification, a denial of service (DoS) attack classification, a distributed denial of service attack (DDoS) attack classification, and/or a ransomware attack classification.
6. The computer-implemented method of claim 1 , further comprising analyzing, via the one or more processors, the predicted future event to determine a solution to the predicted future event.
7. The computer-implemented method of claim 6 , wherein the solution comprises: temporarily suspending a virtual environment account, permanently disabling the virtual environment account, blocking a message in the virtual environment, adding a warning label to the message in the virtual environment, triggering an audio or visual alarm, and/or alerting law enforcement.
8. The computer-implemented method of claim 6 , wherein:
the analyzing the predicted future event comprises routing, via the one or more processors, data of the predicted future event into a trained solution machine learning algorithm to determine the solution to the predicted future event; and
the method further comprises:
presenting, via the one or more processors, the determined solution to an administrator;
receiving, via the one or more processors, and from the administrator, feedback regarding the determined solution; and
routing, via the one or more processors, the feedback to the trained solution machine learning algorithm to further train the solution machine learning algorithm.
9. The computer-implemented method of claim 1 , further comprising:
analyzing, via the one or more processors, the predicted future event to determine a set of solutions to the predicted future event; and
ranking solutions of the set of solutions.
10. A computer system for improving security in a virtual environment by predicting a future event in the virtual environment, the computer system comprising one or more processors configured to:
receive a set of known events including event classifications;
receive data of layers of the virtual environment, wherein the virtual environment enables a user to interact with a virtual world;
detect events in the data of the layers of the virtual environment;
classify the detected events into the event classifications; and
predict the future event by analyzing the detected events by routing the detected events to a trained event machine learning algorithm to predict the future event, wherein the event machine learning algorithm is trained based on data of historical events including layers that the historical events occurred in.
11. The computer system of claim 10 , wherein the predicted future event corresponds to a classification included in the set of known events.
12. The computer system of claim 10 , wherein the one or more processors are further configured to:
analyze the detected events to determine a new event classification; and
wherein the predicted future event corresponds to the new event classification.
13. The computer system of claim 10 , wherein the event classifications in the set of known events include: a character movement classification, a click pattern classification, a message pattern classification, an overlay classification, disorientation classification, an attack on an NFT database classification, a financial fraud classification, a privacy data violation classification, a denial of service (DoS) attack classification, a distributed denial of service attack (DDoS) attack classification, and/or a ransomware attack classification.
14. The computer system of claim 10 , wherein the one or more processors are further configured to analyze the predicted future event to determine a solution to the predicted future event.
15. The computer system of claim 10 , wherein the one or more processors are further configured to:
analyze the predicted future event to determine a set of solutions to the predicted future event; and
rank solutions of the set of solutions.
16. A computing device for improving security in a virtual environment by predicting a future event in the virtual environment, the computing device comprising:
one or more processors; and
one or more memories;
the one or more memories having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing device to:
receive a set of known events including event classifications;
receive data of layers of the virtual environment, wherein the virtual environment enables a user to interact with a virtual world;
detect events in the data of the layers of the virtual environment;
classify the detected events into the event classifications; and
predict the future event by analyzing the detected events by routing the detected events to a trained event machine learning algorithm to predict the future event, wherein the event machine learning algorithm is trained based on data of historical events including layers that the historical events occurred in.
17. The computing device of claim 16 , wherein the predicted future event corresponds to a classification included in the set of known events.
18. The computing device of claim 16 , the one or more memories having stored thereon computer executable instructions that, when executed by the one or more processors, cause the computing device to further:
analyze the detected events to determine a new event classification; and
wherein the predicted future event corresponds to the new event classification.
19. The computing device of claim 16 , wherein the event classifications in the set of known events include: a character movement classification, a click pattern classification, a message pattern classification, an overlay classification, disorientation classification, an attack on an NFT database classification, a financial fraud classification, a privacy data violation classification, a denial of service (DoS) attack classification, a distributed denial of service attack (DDoS) attack classification, and/or a ransomware attack classification.