Method for using generative large language models (LLM) for cybersecurity deception and honeypots
In one aspect, a method for enhancing cybersecurity using Large Language Model (LLM)-generated honeypot schemes, the method includes generating a plurality of deceptive information using an LLM, configured to attract and engage potential attackers, where the plurality of deceptive information includes one or more characteristics referencing vulnerabilities of a network, continuously monitoring for interactions initiated by an interacting party with one or more components of the generated deceptive information, where the interaction is identified as a potential threat to the network, in response to detection of an interaction identified as a potential threat, extracting interaction data associated with the interacting party retrieved during the interaction, and retraining the LLM with the interaction data to create more effective honeypots.
1. A method for enhancing cybersecurity using Large Language Model (LLM)-generated honeypot schemes, the method comprising:
generating a plurality of deceptive information using an LLM, configured to attract and engage potential attackers, wherein the plurality of deceptive information comprises one or more characteristics referencing vulnerabilities of a network;
continuously monitoring for a first interaction initiated by an interacting party with one or more components of the generated deceptive information, wherein the first interaction is identified as a potential threat to the network;
in response to detection of an interaction identified as the potential threat, extracting interaction data associated with the interacting party retrieved during the first interaction; and
retraining the LLM with the interaction data to create more effective honeypot schemes.
2. The method of claim 1 , wherein the LLM-generated honeypot schemes further include a honey pot service configured to prompt the LLM to generate the plurality of deceptive information in accordance with a predetermined script, ensuring consistency with the plurality deceptive information.
3. The method of claim 1 , wherein the plurality of deceptive information includes one or more fabricated user accounts, files, and administrator accounts configured to engage the potential attackers.
4. The method of claim 1 , further comprising:
tokenization of generated deceptive information to provide realistic HTTP responses in response to interactions initiated by the interacting party.
5. The method of claim 1 , wherein, in response to the detection of the interaction identified as the potential threat, the method further includes:
generating one or more remedial measures and policies for the network based on the interaction data, enhancing network security.
6. The method of claim 1 , wherein the continuously monitoring includes generating one or more predictions of a type of interaction to engage the potential threat associated with the one or more characteristics referencing vulnerabilities of the network.
7. The method of claim 1 , wherein the continuously monitoring includes:
generating one or more contextual labels in accordance with contextual data related to the interactions to distinguish and identify threatening interactions from non-threatening interactions; and
retraining the LLM with the one or more contextual labels improving an accuracy level of potential threat detection and effectiveness of the honeypot schemes.
8. The method of claim 1 , further comprising:
training the LLM by leveraging malware data stored in a storage, wherein the LLM references the malware data to identify potential threats in the network.
9. A network device comprising:
one or more memories having computer-readable instructions stored therein; and
one or more processors configured to execute the computer-readable instructions to:
generate a plurality of deceptive information using an LLM, configured to attract and engage potential attackers, wherein the plurality of deceptive information comprises one or more characteristics referencing vulnerabilities of a network;
continuously monitor for a first interaction initiated by an interacting party with one or more components of the generated deceptive information, wherein the first interaction is identified as a potential threat to the network;
in response to detection of an interaction identified as the potential threat, extract interaction data associated with the interacting party retrieved during the first interaction; and
retrain the LLM with the interaction data to create more effective honeypot schemes.
10. The network device of claim 9 , wherein the instructions further cause the one or more processors to:
tokenize generated deceptive information to provide realistic HTTP responses in response to interactions initiated by the interacting party.
11. The network device of claim 9 , wherein in response to the detection of an interaction identified as the potential threat, the instructions further cause the processor to:
generating one or more remedial measures and policies for the network based on the interaction data, enhancing network security.
12. The network device of claim 9 , wherein the continuously monitoring includes:
generating one or more predictions of a type of interaction to engage the potential threat associated with the one or more characteristics referencing vulnerabilities of the network.
13. The network device of claim 9 , wherein the continuously monitoring includes:
generating one or more contextual labels in accordance with contextual data related to the interactions to distinguish and identify threatening interactions from non-threatening interactions; and
retraining the LLM with the one or more contextual labels improving an accuracy level of potential threat detection and effectiveness of the honeypot schemes.
14. A non-transitory computer-readable storage medium comprising computer-readable instructions, which when executed by one or more processors of a network appliance, cause the network appliance to:
generate a plurality of deceptive information using an LLM, configured to attract and engage potential attackers, wherein the plurality of deceptive information comprises one or more characteristics referencing vulnerabilities of a network;
continuously monitor for a first interaction initiated by an interacting party with one or more components of the generated deceptive information, wherein the first interaction is identified as a potential threat to the network;
in response to detection of an interaction identified as the potential threat, extract interaction data associated with the interacting party retrieved during the interaction; and
retrain the LLM with the interaction data to create more effective honeypot schemes.
15. The non-transitory computer-readable storage medium of claim 14 , wherein the LLM-generated honeypot schemes further include a honey pot service configured to prompt the LLM to generate the plurality of deceptive information in accordance with a predetermined script, ensuring consistency with the plurality deceptive information.
16. The non-transitory computer-readable storage medium of claim 14 , wherein the one or more processors are further configured to:
tokenize generated deceptive information to provide realistic HTTP responses in response to interactions initiated by the interacting party.
17. The non-transitory computer-readable storage medium of claim 14 , wherein the one or more processors are further configured to:
generate one or more remedial measures and policies for the network based on the interaction data, enhancing network security.
18. The non-transitory computer-readable storage medium of claim 14 , wherein the continuously monitor includes generate one or more predictions of a type of interaction to engage the potential threat associated with the one or more characteristics referencing vulnerabilities of the network.
19. The non-transitory computer-readable storage medium of claim 14 , wherein the one or more processors are further configured to:
generate one or more contextual labels in accordance with contextual data related to the interactions to distinguish and identify threatening interactions from non-threatening interactions; and
retrain the LLM with the one or more contextual labels improving an accuracy level of potential threat detection and effectiveness of the honeypot schemes.
20. The non-transitory computer-readable storage medium of claim 14 , wherein the one or more processors are further configured to:
train the LLM by leveraging malware data stored in a storage, wherein the LLM references the malware data to identify potential threats in the network.