Honeypot-Based Deception System for Automated Threat Detection

DOI

https://www.doi.org/10.59256/ijire.20260706009

Published In

Volume 7, Issue 6 (2026)

Published Online

November-December 2026

Pages

83-91

Article Type

Original Article

Check for updates

Honeypots and cyber deception are well established methods of detecting suspicious activity and gathering information about attackers. Recent work has moved from traditional high-interaction honeypots toward systems built on LLM based deception systems that offer more realistic and context aware interactions [3], [4]. Autonomous AI agents have been identified as another form of cyber threat [5].Most existing approaches aims to make decoys look realistic, keeping attackers engaged, conserve resources or identify AI driven attacks. This paper proposes an AI Context Window Exhaustion Trap for AI-driven reconnaissance and asks whether carefully controlled deception can increase the contextual and computational workload of a reconnaissance agent while keeping the defender's server resources bounded. The proposed system combines request monitoring, synthetic data generation, suspicious session detection, an isolated deception endpoint, token and workload estimation and server resource tracking. When a session's suspicion score passes an estimated threshold then it is redirected to a controlled decoy environment containing fake or synthetic data. The study examines the response volume, reconnaissance success, estimated context load and defender resource use relate to one another. It aims to test whether context oriented deception can serve against AI-driven reconnaissance while keeping the system stable. This system is proposed as experimental design with no empirical results claimed.