Deployment environment
Lures for network services, protocols and assets, internal or exposed.
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
Paper describing a longitudinal dataset of high-interaction SSH honeypot logs covering connection attempts, authentication brute-forcing and post-authentication command execution. It is intended as a ground-truth source for empirical research and anomaly detection development.
This paper presents a corpus of real scam- and spam-call conversations collected via an active voice-agent honeypot. According to the authors, the system adopts a plausible target persona to sustain interactions with fraudulent callers, recording audio, transcripts, and automatic labels. The study describes the collection system, record structure, and an evaluation of corpus realism and label quality, as well as benchmarking scam detectors trained on synthetic dialogue against real traffic.
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 paper proposes a framework combining game theory with transformer-based components to quantify, model, and optimize cyber deception. The authors define deception attack-surface metrics and embed them in a signaling game, aiming to guide adaptive deception strategies.
The authors present a multi-attacker Bayesian Stackelberg framework for allocating honeypots in a directed network, modeling each attacker as a follower with a latent type and the defender as replanning via a DOBSS-style mixed-integer linear program under posterior beliefs. They aim to scale defense to realistic graphs using layered structures and pruning.
The authors present AdvancedShelLM, an LLM-based SSH honeypot using a multi-agent architecture (Manager and Worker) intended to improve command understanding and deceptive capability over its predecessor shelLM. According to the abstract, the system was evaluated through unit tests, an AI attacker, human attackers, and an Internet deployment.