Cisco Technology, Inc. (20240333765). METHOD FOR USING GENERATIVE LARGE LANGUAGE MODELS (LLM) FOR CYBERSECURITY DECEPTION AND HONEYPOTS simplified abstract
Contents
METHOD FOR USING GENERATIVE LARGE LANGUAGE MODELS (LLM) FOR CYBERSECURITY DECEPTION AND HONEYPOTS
Organization Name
Inventor(s)
David Arthur Mcgrew of Poolesville MD (US)
Hugo Mike Latapie of Long Beach CA (US)
Blake Anderson of Chapel Hill NC (US)
METHOD FOR USING GENERATIVE LARGE LANGUAGE MODELS (LLM) FOR CYBERSECURITY DECEPTION AND HONEYPOTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240333765 titled 'METHOD FOR USING GENERATIVE LARGE LANGUAGE MODELS (LLM) FOR CYBERSECURITY DECEPTION AND HONEYPOTS
The method described in the abstract involves using large language models (LLMs) to create deceptive information that mimics vulnerabilities in a network, attracting potential attackers and monitoring their interactions to identify threats.
- Generating deceptive information with LLMs to attract potential attackers.
- Monitoring interactions with the deceptive information to detect potential threats.
- Extracting data from interactions with potential threats to retrain the LLM for more effective honeypots.
Potential Applications: - Enhancing cybersecurity measures by proactively identifying and deterring potential threats. - Improving network security by using advanced technology to create effective honeypot schemes.
Problems Solved: - Addressing the challenge of identifying and mitigating cybersecurity threats in real-time. - Enhancing the effectiveness of cybersecurity measures by utilizing AI-generated deceptive information.
Benefits: - Increased protection against cyber threats through proactive deception techniques. - Improved response to potential security breaches by continuously monitoring and adapting honeypot schemes.
Commercial Applications: Title: Advanced Cybersecurity Solutions Using LLM-Generated Honeypots Description: This technology can be utilized by cybersecurity firms, government agencies, and businesses to enhance their security measures and protect sensitive data from cyber threats. The market implications include increased demand for advanced cybersecurity solutions and services.
Questions about the technology: 1. How does the use of LLMs improve the effectiveness of honeypot schemes in cybersecurity? 2. What are the potential limitations or challenges associated with implementing LLM-generated honeypots in a network security system?
Original Abstract Submitted
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.
