Leveraging generative AI for dynamic file honeypots: Insights and implementation

Vladimir Strogov and Sergey Ulasen
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Vladimir Strogov: Acronis, 8 Temasek Boulevard #30-01/02 Suntec Tower 3, Singapore
Sergey Ulasen: Constructor Technology, Singapore

Cyber Security: A Peer-Reviewed Journal, 2025, vol. 8, issue 4, 392-400

Abstract: In the field of cyber security, dynamic file honeypots serve as critical tools for detecting and analysing malicious activities, especially zero-day ransomware attacks. This paper explores the integration of generative AI (GenAI) with file honeypots to enhance their effectiveness. By dynamically generating virtual decoy content, these advanced honeypots deceive attackers and protect real data. The discussion includes technical aspects of implementing file honeypots, focusing on two primary cases: dynamically inserted virtual honeypot files and honeypot files wrapped around real protected files. The paper also outlines the benefits of using GenAI for realistic content generation, continuous adaptation and enhanced threat intelligence. This approach not only bolsters security measures but also reduces storage footprints and improves overall cyber security resilience.

Keywords: leveraging generative AI; dynamic file honeypots; LLM for honeypot generation; malicious thread injection; dynamically inserted virtual honeypot files; advanced file system integration (search for similar items in EconPapers)
JEL-codes: M15 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:aza:csj000:y:2025:v:8:i:4:p:392-400

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