Paper2024
Material reviewed: AbstractAccording to the authors, this paper explores the use of Generative Adversarial Networks (GANs) to generate decoy configurations for cyber defense. They note that existing approaches rely on maintained lists of configurations or pre-configured images with limited adaptability, and present what they describe as the first attempt to use GANs specifically for generating decoy configurations, aiming to provide defenders with a tool to strengthen network defenses.
NetworkHoneypotDecoy
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: AbstractThis paper proposes a reinforcement learning (RL) approach to simulate ransomware attacks within an environment mirroring real-world networks, aiming to streamline traditional red teaming exercises. According to the authors, experimental results on a 152-host example network show the RL agent's capability to discover and orchestrate attacks on high-value targets while evading honeyfiles (decoy files placed to detect unauthorized access).
NetworkEndpointDecoyAdversary engagement
Paper2024
Material reviewed: AbstractThe authors propose LLMPot, an approach for designing honeypots in Industrial Control System (ICS) networks using large language models (LLMs). The goal is to automate the creation of realistic, vendor-agnostic honeypots capable of emulating industrial protocols and PLC control logic, reducing the manual effort traditionally required in this domain.
OT/ICSNetworkHoneypotDecoy
Paper2024
Material reviewed: AbstractThe work presents and evaluates Mirage, an experimentation system in both emulation and simulation forms that allows for the implementation and testing of cyber deceptions designed to counter adversaries using AI search and planning capabilities. The authors frame cyber deception as a possible defensive mechanism against future AI-enabled adversaries.
NetworkDecoyAdversary engagement
Paper2024
Material reviewed: AbstractThis work aims to develop a drone-based surveillance mission system when Denial-of-Service (DoS) attacks disrupt normal operations. The authors introduce cyber deception via honey drones (HDs) that exhibit fake vulnerabilities and use stronger signal strengths to lure DoS attacks, formulate an optimization problem over HD and mission drone signal strengths, and solve it using deep reinforcement learning (DRL) with the Asynchronous Advantage Actor-Critic (A3C) algorithm, evaluated in a physical engine-based simulation testbed.
HoneypotDecoy
Paper2024
Material reviewed: AbstractThe study describes a two-tier deception-driven security model intended to mislead potential attackers and protect network resources using decoys, honeypots, and deceptive network segments. The authors report on deploying and testing the model to assess its efficacy and feasibility in practice, and argue that integrating deception techniques into cybersecurity frameworks can provide a protective layer.
NetworkHoneypotDecoy
Paper2024
Material reviewed: AbstractEditor contributionPacheco and Staino preprint proposing a cyclical, minimal deception process and presenting DOLOS-T as an open tool for exploring it.
NetworkApplicationDecoyAdversary engagement
Paper2024
Material reviewed: AbstractAccording to the authors, this chapter examines the combined use of game-theoretic models and foundation models (FMs) to analyze, design, and implement cyber deception tactics across tactical, operational, and strategic warfare levels. It proposes MANSCOL (multi-agent neurosymbolic conjectural learning) to let defenders predict adversarial behaviors and design adaptive deceptive defenses, and discusses challenges of applying FMs in cybersecurity.
NetworkAdversary engagementDecoy
Paper2024
Material reviewed: AbstractThis paper presents a proof-of-concept (POC) cyber deception system that dynamically generates deception material based on observed attacker behaviour during a simulated real-time attack. The system analyzes attacker interaction with the deception material to predict the attacker's motive, and the authors explore expanding this POC to infer other profile features such as psychological characteristics.
NetworkDecoyAdversary engagement
Paper2024
Material reviewed: AbstractThis work-in-progress paper studies the implementation of deception-in-depth strategies with three initial logical layers: network, host, and data. The authors draw on ideas from military deception, network orchestration, software deception, file deception, fake honeypots, and moving-target defenses, and are building a prototype to test in adversarial environments. According to the authors, the goal is to show that deploying a broad range of deception techniques can better protect systems than single techniques, with a focus on discouraging attacks rather than encouraging attacker engagement.
NetworkEndpointHoneypotDecoy
Paper2023
Material reviewed: Full textCritical readingSurvey of network requirements for implementing cyber deception techniques.
NetworkDecoy
Paper2023
Material reviewed: AbstractThe work proposes a decision support system based on a Bayesian belief network to guide cyber deception strategies from a detailed model of attacker beliefs, illustrated with a case study using passively observed operating system fingerprinting data. The authors compare the model's prediction accuracy with a sequential artificial neural network and introduce a deception algorithm to select a minimal set of features whose modification would make a system appear as a different operating system.
EndpointNetworkDecoy
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 authors present a method for selecting cyber deception strategies using a multi-stage Flipit game, aiming to account for defense timing alongside spatial strategy selection. The approach constructs a spatiotemporal decision-making model, introduces a discount factor and transition probabilities, and applies a Proximal Policy Optimization algorithm to compute defender strategies.
NetworkDecoyMoving target defense
Paper2023
Material reviewed: AbstractThe authors propose a signaling game framework between a defender and an insider human agent, using quantum decision theory to exploit cognitive biases and counter insider attacks. The defender deceives the attacker by luring them toward decoy sensors via generators that produce perceptions of classical signals. According to the authors, results show that strategically designed generators can worsen the attacker's ability to identify decoys compared to schemes without generators.
IdentityDecoyAdversary engagement
Paper2023
Material reviewed: AbstractThe authors present HoneyEVSE, a honeypot designed to simulate Electric Vehicle Supply Equipment (EVSE) within the Vehicle-to-Grid paradigm. The system emulates the charging process and login pages with high fidelity to increase attacker engagement. They exposed the honeypot for 30 days on the Internet and report it successfully evaded the Shodan Honeyscore metric while attracting numerous interactions.
IoTOT/ICSHoneypotDecoy
Paper2023
Material reviewed: AbstractThe authors propose a game-theoretic approach for optimal honeypot allocation in tactical networks with dynamic connectivity. They model a two-player dynamic game accounting for future network state evolution, aiming to maximize the attacker's likelihood of hitting a honeypot while minimizing reconfiguration costs. They present an iterative algorithm to find Nash equilibrium strategies and evaluate its scalability through simulations.
NetworkHoneypotDecoy
Paper2023
Material reviewed: AbstractThis report reviews the concepts and types of honeypots, which organizations use as decoy networks to lure attackers, and discusses challenges of deploying them along with advantages and disadvantages.
NetworkHoneypotDecoy
Paper2023
Material reviewed: AbstractThe work addresses honeyword generation: fake passwords that accompany the real password (the "sugarword") in a password-cracking detection system, where a honeychecker distinguishes the real password from the fake ones. The authors describe a honeyword generation method based on a proposed discrete salp swarm algorithm (SSA), a bio-inspired metaheuristic, and present the proposed methodology alongside experimental results compared with previous strategies.
IdentityHoneytokenDecoy
Paper2023
Material reviewed: AbstractThe authors propose a ransomware detection method based on behavior and deception, using decoy files and directories to reveal malicious activity. The paper describes a pilot study with Locky.
Decoy
Paper2023
Material reviewed: AbstractThe authors propose a deep learning based method to generate synthetic IoT device traffic flows mimicking real traffic, aiming to improve IoT honeypots against attacker detection. According to the abstract, the generator outperforms prior approaches in remaining indistinguishable from real traffic.
IoTNetworkHoneypotDecoy
Paper2023
Material reviewed: Publisher metadataBibliographic record on deception against autonomous attacks. The abstract and findings still require review.
NetworkDecoy
Paper2023
Material reviewed: AbstractThe authors investigate using ChatGPT to automatically reply to scam emails in order to waste scammers' time and resources through pointless communications. According to the abstract, preliminary results show ChatGPT can decoy scammers.
ApplicationDecoyAdversary engagement