Deployment environment
Lures representing connected devices and protocols, with attention to isolation and telemetry.
Research question
Definitions and technical considerations should be checked against the primary source. Atlas records are a selection tagged with this topic; a matching tag does not demonstrate operational outcomes.
Cross-reference
These counts describe tagged Atlas records, not the worldwide number of studies or offerings.
Linked library
The authors propose TAB-DEN, a framework for IoT healthcare systems that combines a permissioned blockchain with Proof of Authority consensus for decentralized authentication, a hierarchical edge architecture of primary and secondary edge servers that compute trust scores in real time and redirect traffic according to trustworthiness, and deception-based edge nodes acting as honeypot-like environments to attract, isolate and profile attackers. The stated aim is to provide scalable and quantum-resistant security, recording intrusion events through a lattice-based post-quantum signature scheme so that attack records are immutable and verifiable on the distributed ledger.
The authors propose a dynamic honeypot framework combining a reinforcement-learning deception agent (tabular Q-learning, formalized as an MDP), threat-intelligence fusion via indicators of compromise, and automated containment actions. Its purpose is to adapt the honeypot's exposed persona and guide deception decisions, evaluated in an isolated virtualized testbed.
The paper introduces Co-ADAM, a defense framework that formalizes cyber deception in the Industrial Internet of Things (IIoT) as a constrained Markov decision process with an embedded signaling game, aiming to jointly model the deception investment level and a co-evolutionary adversary that accumulates skepticism. The authors describe training a deep Q-network (DQN) defender against an adaptive attacker pool with budget constraints and skepticism dynamics embedded in the environment, and report evaluation on several IIoT datasets and a physical testbed.
The authors propose using honey drones as a deception strategy to defend unmanned aerial vehicle (UAV) mission systems against denial-of-service attacks. They formulate an attacker-defender optimization problem based on radio signal strength and present an approach called HT-DRL, combining hypergame theory with deep reinforcement learning to identify optimal deception strategies.
This paper presents LDT-FRL, a defense framework combining temporal attention encoders, lightweight digital twins, and a federated reinforcement learning (PPO) agent capable of redirecting suspicious traffic to honeypots in IoMT (Internet of Medical Things) environments. According to the authors, the system was evaluated on public network traffic benchmarks, achieving high classification metrics and explainability via techniques such as SHAP and Grad-CAM.
The paper proposes HIVE-O, a decentralized honeypot framework for IoT ecosystems, with lightweight distributed nodes that capture, analyze and share attack intelligence without a single point of control. The authors describe emulation of IoT services and protocols and an experimental evaluation of reconnaissance, exploitation and denial-of-service attempts.