Paper2026
Material reviewed: AbstractThe 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.
NetworkCloudHoneypotAdversary engagement
Paper2026
Material reviewed: AbstractThe study compares, under homogeneous conditions, the technical performance, resource consumption and operational costs of traditional honeypots versus honeypots that incorporate machine learning to improve realism and hinder fingerprinting. The authors deployed six open-source honeypots simultaneously (three traditional and three ML-enhanced) for 60 days on Hetzner Cloud infrastructure, reporting differences in useful interaction, fingerprinting and return on investment.
CloudHoneypot
Paper2026
Material reviewed: AbstractPresents the design, implementation, and evaluation of a honeypot monitoring system deployed on AWS that emulates SSH, HTTP, and HTTPS services on EC2 instances within an isolated virtual private cloud. The system aims to attract, record, and analyze malicious interactions, using a Python/Flask backend and a React/D3.js dashboard for attack visualization.
CloudNetworkHoneypotDecoy
Paper2026
Material reviewed: AbstractThe paper describes the design of a honeypot framework deployed in a cloud environment to support threat detection and incident response. According to the authors, it integrates Microsoft Azure services to monitor and analyze malicious behavior, using telemetry, threat intelligence and automated workflows.
CloudHoneypot
Paper2026
Material reviewed: AbstractThis data article presents MURHCAD, a high-resolution honeynet dataset collected over 72 hours in June 2025 on Microsoft Azure, using three honeypots (Cowrie, Dionaea, and SentryPeer) deployed across four geographically dispersed virtual machines. According to the authors, the dataset comprises 132,425 attack events with enriched metadata (timestamps, IPs, geolocation, ports, and protocols) intended to support anomaly detection, protocol-misuse studies, and threat intelligence research.
CloudNetworkHoneypot
Paper2026
Material reviewed: AbstractThis report addresses the challenge of detecting AI-driven offensive cyber agents, proposing a strategic 'detection-in-depth' framework. According to the authors, five detection mechanisms are presented, including agent identifiers and agent honeypots, intended to support policymakers and industry against this emerging threat.
NetworkCloudHoneypotAdversary engagement
Paper2026
Material reviewed: AbstractThe authors present HoneyRoute, an inference-serving layer that detects malicious requests and routes them to a dedicated honeypot model, shielding the production model while harvesting attacker intelligence. The system combines a streaming router, a dual-implementation honeypot, and an analysis loop that feeds back into router retraining.
ApplicationCloudHoneypotDecoy
Paper2026
Material reviewed: AbstractThe paper proposes an LLM-enabled cloud-native dynamic honeypot architecture in which the model acts only as a text synthesizer behind policy gating and state verification. The authors describe a session broker that classifies attacker commands rather than executing them, alongside ethical governance controls and an evaluation protocol.
CloudNetworkHoneypotAdversary engagement
Paper2026
Material reviewed: AbstractThis paper presents Ollure, a low- and medium-interaction honeypot that emulates the Ollama API without a backend LLM, as described by the authors. Deployed across four locations over 84 days, it recorded hundreds of thousands of interactions from thousands of source IP addresses. The study documents automated discovery and fingerprinting activity, as well as concrete exploitation attempts against both the infrastructure and LLM layers.
NetworkApplicationHoneypotDecoy
Paper2025
Material reviewed: AbstractThis paper introduces an automated cloud defense framework that leverages medium- to high-interaction honeypot telemetry to dynamically update firewall rules in real time. According to the authors, the system integrates the Cowrie honeypot, Azure-native automation tools, and MITRE ATT&CK-aligned classification within a closed-loop feedback mechanism. The authors report an average Mean Time to Block of 0.86 seconds and classification of over 12,000 SSH attempts in experimental testing.
CloudNetworkHoneypotAdversary engagement
Paper2025
Material reviewed: Public pageThe paper studies files uploaded by attackers to a cloud honeypot and uses them to compare signature-based detection by antivirus programs.
CloudHoneypot
Paper2025
Material reviewed: AbstractThe authors present Koney, a Kubernetes operator that orchestrates cyber deception technology deployment through deception policy documents described as code. Using cloud-native technologies such as service meshes and eBPF, the system adds traps to containerized applications without source code access, facilitating setup, rotation, monitoring, and removal of traps, emphasizing maintainability, scalability, and operational simplicity.
CloudApplicationDecoyAdversary engagement
Paper2025
Material reviewed: AbstractThe authors present ShadowKube, an active defense framework for Kubernetes that integrates behavioral monitoring with shadow honeypots to detect and neutralize anomalous behavior. The approach establishes behavioral baselines to identify deviations and converts compromised nodes into honeypots, with the stated purpose of isolating and trapping attackers in intrusions targeting container orchestration.
CloudApplicationHoneypotDecoy
Paper2024
Material reviewed: Full textCritical readingSurvey proposing a taxonomy and comparing cyber deception research lines, including AI-based work.
NetworkCloudAdversary engagement
Paper2024
Material reviewed: AbstractThe paper assesses a specific group of honeypot systems based on criteria such as detection range, emulation accuracy, data quality, dependability, scalability, and ease of setup, and draws conclusions about their relative effectiveness in different situations. It also discusses recent developments in honeypot technology, including machine learning enhancements, increased automation, integration with other security tools, cloud-based honeypots, and deception technology, using simulated attacks to demonstrate how these systems log and record attack information.
NetworkCloudHoneypotDecoy
Paper2024
Material reviewed: AbstractThe work addresses automating honeypot deployment using Ansible and a web application that also monitors the process. The authors describe their purpose of reducing the time and resources of manual deployment and report tests on sensors running on Google Cloud Platform.
CloudHoneypot
Paper2023
Material reviewed: AbstractThe work describes the deployment of an ensemble of research honeypot detectors to analyze and evaluate attackers' behavior. The authors present an outline of a containerized cloud honeypot deployment that they state is portable, durable, and simple to deploy and administer, and they describe instrumented monitoring that generated data points from which inferences about malicious users' activities and purpose could be drawn.
CloudNetworkHoneypotDecoy
Paper2023
Material reviewed: AbstractThe paper describes implementing a honeypot on Azure Services as a supplement to customary cloud security measures, listing advantages such as prompt attack detection, recognition of attack methods, and safeguarding critical data. The authors outline steps including selecting a virtual machine, installing and configuring honeypot software, deploying monitoring and logging, establishing security groups, running a penetration test, and consistently monitoring the honeypot.
CloudHoneypot
Paper2023
Material reviewed: AbstractThis thesis presents a honeypot solution to investigate malicious activity in the heiCLOUD environment, showing according to the author a significant increase in attacks. A new concept is proposed to detect firewall leaks in restricted network zones, along with a method for detecting honeypots at the transport level from an attacker's perspective. A customized OpenSSH server is also presented as an intermediary instance to mitigate such efforts.
CloudNetworkHoneypotAdversary engagement
Paper2022
Material reviewed: AbstractThe article proposes EA-POT, an explainable AI-assisted permissioned blockchain framework for predicting potential defaulters' IP addresses, registering those IPs in a blockchain database based on explainable AI suggestions and IP authorizer approval to enhance immutability. The authors carried out experiments in an IoT Cloud research laboratory using three prediction models (Random Forest, Linear Regression, and SVM) and describe a procedure that incorporates interpretable knowledge while blacklisting IPs that reach HoneyPots.
IoTCloudHoneypot
Paper2022
Material reviewed: AbstractThe paper addresses intrusion detection and cloud security and proposes using honeypots to divert malicious traffic away from systems. The authors describe implementing the honeypot concept in a file-sharing application deployed on a cloud server, aiming to detect attacks and improve security in that environment.
CloudApplicationHoneypot
Paper2022
Material reviewed: AbstractThe paper describes a security framework for multi-cloud environments aimed at diverting an attacker and gaining time to analyze and mitigate intrusions. The proposed mechanism consists of modules for managing the multi-cloud platform, an intrusion detection and prevention system, and honeypots, with detection results compared in a two-phase scenario.
CloudNetworkHoneypotDecoy
Paper2022
Material reviewed: AbstractThe paper describes setting up a honeypot on a cloud service and analyzing the activity it captures. It then applies machine learning algorithms to predict the type of threat detected in the honeypots.
CloudHoneypot
Paper2021
Material reviewed: AbstractThe authors propose a defensive deception framework for the container-based cloud, aiming to reveal stealthy attackers by presenting intentionally falsified information. The approach formalizes a System Risk Graph (SRG) as an updatable adversarial model, trains a deep reinforcement learning (DRL) model over the SRG so the agent produces decoy placement and deceptive routing strategies, and defines a coefficient of deception to evaluate the placement strategy.
CloudDecoyAdversary engagement