LLM-Generated Honeypots for Evolving Attack Detection
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Solution Overview
Problem
Traditional cybersecurity measures are inadequate in detecting and thwarting sophisticated cyber threats, particularly those involving chatbots mimicking human interactions, due to the evolving tactics of malicious actors and expanding digital interactions, making it difficult to distinguish between legitimate and malicious activities.
Innovation Solution
Implementing Large Language Model (LLM)-generated honeypot schemes that create deceptive documents, accounts, and users to proactively lure attackers, using fabricated HTTP requests and responses, and continuously monitor and retrain the LLM with interaction data to enhance threat detection and security measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cybersecurity measures are used, then basic security coverage is maintained, but detection of sophisticated cyber threats is inadequate
Solution Approach 1:
The system proactively creates deceptive honeypot content (fake documents, accounts, data) before actual attacks occur, using LLMs to generate realistic-looking but fraudulent digital artifacts that attackers might target. This preliminary deployment of decoys enables early detection of malicious intent before real systems are compromised.
Solution Approach 2:
The system creates copies of legitimate digital content (documents, accounts, data structures) using LLMs to generate highly realistic fake versions that mirror the characteristics of authentic materials. These copies serve as honeypots that attract and reveal attackers without exposing real assets.
2Reliability
If honeypot schemes are implemented, then attacker resources are diverted, but continuous monitoring and retraining are required
Solution Approach 1:
The system continuously monitors interactions with honeypot content and feeds this data back to the LLM for retraining. Attack behavior patterns captured through monitoring are used to improve the realism and effectiveness of future honeypot generations, creating a self-improving security system that adapts to emerging threats.
Solution Approach 2:
The LLM autonomously generates new honeypot content based on learned attack patterns without requiring manual intervention for each threat scenario. The system self-manages the creation and deployment of deceptive content, reducing operational complexity while maintaining high adaptability.
3Measurement precision
If LLM-generated deceptive information is used, then threat detection accuracy is improved, but resource consumption increases
Solution Approach 1:
The system uses LLMs to generate only the specific honeypot content needed for particular threat scenarios rather than comprehensively securing all digital assets. By focusing computational resources on creating targeted deceptive content in high-risk areas, the system achieves high detection accuracy without excessive overall resource consumption.
Data Source
AI summary
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.