# Cyber Deception: State of the art, Trends and Open challenges

arXiv: https://arxiv.org/abs/2409.07194 · HTML: https://mlsanity.com/paper/2409.07194/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 201 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Builds on (most cited first, 23 of 23)

- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [Deep Reinforcement Learning for Cyber Security](https://mlsanity.com/paper/1906.05799/) (2019)
- [A Game-Theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy](https://mlsanity.com/paper/1712.05441/) (2017)
- [Review of Generative AI Methods in Cybersecurity](https://mlsanity.com/paper/2403.08701/) (2024)
- [A Differentially Private Game Theoretic Approach for Deceiving Cyber Adversaries](https://mlsanity.com/paper/2008.05629/) (2020)
- [Combating Advanced Persistent Threats: Challenges and Solutions](https://mlsanity.com/paper/2309.09498/) (2023)
- [DOLOS: A Novel Architecture for Moving Target Defense](https://mlsanity.com/paper/2303.00387/) (2023)
- [Demystifying Deception Technology:A Survey](https://mlsanity.com/paper/1804.06196/) (2018)
- [Cyber Deception for Computer and Network Security: Survey and Challenges](https://mlsanity.com/paper/2007.14497/) (2020)
- [Security Orchestration, Automation, and Response Engine for Deployment of Behavioural Honeypots](https://mlsanity.com/paper/2201.05326/) (2022)
- [Deception for Cyber Defence: Challenges and Opportunities](https://mlsanity.com/paper/2208.07127/) (2022)
- [A Game-Theoretic Study on Non-Monetary Incentives in Data Analytics Projects with Privacy Implications](https://mlsanity.com/paper/1505.02414/) (2015)
- [Preventive and Reactive Cyber Defense Dynamics Is Globally Stable](https://mlsanity.com/paper/1602.06807/) (2016)
- [Optimal deception attack on networked vehicular cyber physical systems](https://mlsanity.com/paper/2002.01545/) (2020)
- [An Analytics Framework for Heuristic Inference Attacks against Industrial Control Systems](https://mlsanity.com/paper/2101.11866/) (2021)
- [Three Decades of Deception Techniques in Active Cyber Defense -- Retrospect and Outlook](https://mlsanity.com/paper/2104.03594/) (2021)
- [HoneyCar: A Framework to Configure Honeypot Vulnerabilities on the Internet of Vehicles](https://mlsanity.com/paper/2111.02364/) (2021)
- [Sludge for Good: Slowing and Imposing Costs on Cyber Attackers](https://mlsanity.com/paper/2211.16626/) (2022)
- [SCANTRAP: Protecting Content Management Systems from Vulnerability Scanners with Cyber Deception and Obfuscation](https://mlsanity.com/paper/2301.10502/) (2023)
- [AIIPot: Adaptive Intelligent-Interaction Honeypot for IoT Devices](https://mlsanity.com/paper/2303.12367/) (2023)
- [Honeypot Allocation for Cyber Deception in Dynamic Tactical Networks: A Game Theoretic Approach](https://mlsanity.com/paper/2308.11817/) (2023)
- [HoneyDOC: An Efficient Honeypot Architecture Enabling All-Round Design](https://mlsanity.com/paper/2402.06516/) (2024)
- [Cyber Deception Reactive: TCP Stealth Redirection to On-Demand Honeypots](https://mlsanity.com/paper/2402.09191/) (2024)

## Abstract (arXiv)

The growing interest in cybersecurity has significantly increased articles designing and implementing various Cyber Deception (CYDEC) mechanisms. This trend reflects the urgent need for new strategies to address cyber threats effectively. Since its emergence, CYDEC has established itself as an innovative defense against attackers, thanks to its proactive and reactive capabilities, finding applications in numerous real-life scenarios. Despite the considerable work devoted to CYDEC, the literature still presents significant gaps. In particular, there has not been (i) a comprehensive analysis of the main components characterizing CYDEC, (ii) a generic classification covering all types of solutions, nor (iii) a survey of the current state of the literature in various contexts. This article aims to fill these gaps through a detailed review of the main features that comprise CYDEC, developing a comprehensive classification taxonomy. In addition, the different frameworks used to generate CYDEC are reviewed, presenting a more comprehensive one. Existing solutions in the literature using CYDEC, both without Artificial Intelligence (AI) and with AI, are studied and compared. Finally, the most salient trends of the current state of the art are discussed, offering a list of pending challenges for future research.