Method for using generative large language models (llm) for cybersecurity deception and honeypots

Abstract

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.