Paper2022
Material reviewed: AbstractThe study aims to secure IoT devices by using a Raspberry Pi as a honeypot to imitate those devices, check user intent, and analyze attack patterns. The authors implemented the Cowrie, Dionaea, and Glastopf honeypots on Raspberry Pi 4B to cover vulnerable protocols and record attacks.
IoTHoneypot
Paper2022
Material reviewed: AbstractAccording to the authors' abstract, this paper uses attack-defense games to examine whether disguising normal servers as honeypots is beneficial. It compares three scenarios with varying levels of deception techniques and concludes, per the authors, that disguising real servers does not always yield better defender rewards and may increase the cost of maintaining normal nodes.
NetworkHoneypotDecoy
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: AbstractThis work proposes EVHS, an elastic virtual honeypot system for SDNFV-based networks, aimed at flexibly managing and controlling honeypots. The system uses a network intrusion detection system at the network border to detect attacks, leverages SDN and NFV to generate honeypots, and employs a moving-target defense mechanism to connect attackers to these honeypots, while also reusing available honeypots after attacks are handled.
NetworkHoneypotMoving target defense
Paper2022
Material reviewed: AbstractThis survey reviews and categorizes honey-X-based defense strategies, such as honeypots and honeytokens, which use deception to gather threat intelligence and protect networks. The authors also examine their combination with moving target defense, game theory and machine learning, and outline security quantification, challenges and future directions.
NetworkHoneypotHoneytoken
Paper2022
Material reviewed: AbstractThis study examines how the proportion of honeypots deployed in a network influences an adversary's decisions, using a deception game with two consecutive stages: probe and attack. Participants were randomly assigned to conditions with small, medium, or large honeypot proportions, and the authors report that as the honeypot proportion increased, honeypot-directed and no-attack actions increased dramatically.
NetworkHoneypotAdversary engagement
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: AbstractThis paper describes the deployment of medium-interaction honeypots across five different protocols on the public Internet to study the intent and sophistication of observed attacks. The authors propose a clustering approach that identifies correlations in attacker behavior to detect IP addresses likely controlled by a single operator, highlighting the usefulness of these honeypots for data collection.
NetworkHoneypot
Paper2022
Material reviewed: AbstractThe authors propose a text-based method to investigate and analyze bot loaders using high-interaction honeypots. They collect request logs, define eight loader families via agglomerative clustering, and analyze functional homology using sequence alignment techniques, revealing, according to the study, evolutionary relationships among botnet infrastructures such as Mirai.
NetworkHoneypot
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: AbstractThis paper reviews existing tools for monitoring P2P botnets, categorizing mechanisms as active (crawlers) and passive (sensors and honeypots). According to the authors, the purpose is to study ways to monitor a botnet and how these monitoring mechanisms work, presenting prior work on each approach.
NetworkHoneypot
Paper2022
Material reviewed: AbstractReview article that provides an overview of cyber security and discusses machine learning, cyber threats and honeypot system-based techniques for intrusion detection and the study of network attacks. The authors describe honeypots as conceptual traps intended to block unauthorised access and to give analysts insights into the origins and behaviours of such attacks.
NetworkHoneypot
Paper2022
Material reviewed: AbstractThe work proposes integrating a honeypot sensor with a software-defined network (SDN) and classifying distributed denial-of-service attacks via semi-supervised learning, combining a pseudo-labelling model based on a support vector machine with subsequent classification using Adaptive Boosting. The evaluation reported by the authors uses monitoring data from the Suricata sensor and examines accuracy, precision, recall, CPU usage, and flow-rule mitigation times.
NetworkHoneypot
Paper2022
Material reviewed: AbstractThe authors describe a Spark-based honeypot system designed, implemented, and tested on a campus network over several days, intended to draw intruders toward a resource that simulates a fully resourced computer or vault. The study presents a performance analysis of that deployment based on parameters defined by the authors.
NetworkHoneypot
Paper2022
Material reviewed: AbstractThe authors describe a TaintDroid-based honeypot monitoring system for embedded devices, in which TaintDroid marks attackers who break into the honeypot and their behavior is then monitored and recorded. The work also reports a feasibility test of the system built by the authors.
IoTEndpointHoneypot
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
Paper2022
Material reviewed: Public pageThe paper proposes an intrusion detection framework combining CAPTCHA, honeypots and supervised learning. Honeypots would be integrated into a website to lure malicious software, and the diagram sketches realistic decoy pages and decoy fields.
ApplicationNetworkHoneypotDecoy
Paper2022
Material reviewed: AbstractThis paper introduces TSM (Topic Semantic Matching), a metric based on topic modelling and semantic matching in embedding spaces to measure the enticement of honeyfiles relative to local content. According to the authors, it is the first measure using NLP techniques to quantify this enticement robustly against paraphrasing, supported by a honeyfile corpus built with various NLP methods.
EndpointHoneypotDecoy
Paper2022
Material reviewed: AbstractThis work models attacker-defender engagements as Markov Games to understand human adversarial behaviors and optimize defensive strategy. The authors validate their approach using a Capture-The-Flag (CTF) setup and user studies across varying skill levels. According to the authors, application-level deceptions are an optimal mitigation strategy against targeted attacks, outperforming classic defensive maneuvers, and they present an analysis of attacker behavior when trapped in an embedded honeypot environment.
ApplicationHoneypotAdversary engagement
Paper2021
Material reviewed: AbstractThe authors propose a multiphase dynamic deployment mechanism for virtualized honeypots (MD2VH) based on intelligent attack path prediction, modeling attack and defense characteristics with a Bayesian state attack graph and a multiphase deployment optimization model based on an extended Markov decision process, and generating deployment strategies by combining online and offline reinforcement learning. They implement a prototype system on software-defined networking and virtualization containers and report experiments on capture rate under simple and complex topologies.
NetworkHoneypotMoving target defense
Paper2021
Material reviewed: AbstractThe authors present a low-cost honeypot capable of blocking commands or replying with insults to attackers, modeling the attacker-defender interaction as a Bayesian two-player game. According to the abstract, three honeypot instances were deployed and over 200 sessions were captured for a qualitative analysis of post-exploitation behavior.
NetworkHoneypotAdversary engagement
Paper2021
Material reviewed: AbstractThis paper presents, according to its authors, a machine learning-based method for dynamic configuration, deployment and maintenance of honeypots in networks. The approach identifies and clusters machines and devices to intelligently deploy honeypots, reducing the need for manual configuration.
NetworkHoneypot
Paper2021
Material reviewed: AbstractThe authors propose a low-interaction, adaptive and dynamic web application honeypot that imitates vulnerabilities through HTTP events, using the SNARE and TANNER tools to generate the attack surface and analyze/classify incoming requests, as described in the abstract.
ApplicationHoneypot
Paper2021
Material reviewed: AbstractAccording to the authors, this work studies two-sided deception in cybersecurity by manipulating the observable configuration of both honeypots and real machines. Using an experiment with a 40-machine network (20 honeypots) and an experimental testbed, the authors compare three feature-concealment conditions against human attackers.
NetworkHoneypotDecoy