CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 13176 > Article
Paper
22 May 2024 VDH: a dynamic honeynet technology based on game theory
Li Ma, Ming Chen, Liang Liu
Author Affiliations +
Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131761P (2024) https://doi.org/10.1117/12.3029002
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China
Abstract
As the complexity of cyber attacks increases, honeypot networks, acting as a proactive security mechanism, have become an important area of research in the field of cybersecurity. Although traditional honeynets offer a degree of defense, their limitations in dealing with complex and persistent cyber attacks, including a lack of flexibility, insufficient allure for attackers, and high maintenance costs, are increasingly exposed. This paper introduces a versatile dynamic honeynet architecture (VDH) to compensate for these deficiencies in existing honeynet technologies. By deploying in parallel with enterprise networks and dynamically generating honeypot nodes, the VDH architecture achieves flexible control and redirection of suspicious traffic, thus enhancing network protection capabilities. The paper further abstracts honeynet attack and defense behaviors into a non-cooperative incomplete information game model, and through precise modeling and analysis of the single-stage attack-defense game process, proposes a novel optimal configuration strategy for dynamic honeynets. Lastly, the paper designs and implements the VDH prototype system based on network function virtualization, and verifies the effectiveness and superiority of the VDH structure and configuration strategy.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Li Ma, Ming Chen, and Liang Liu "VDH: a dynamic honeynet technology based on game theory", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131761P (22 May 2024); https://doi.org/10.1117/12.3029002
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 9 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Network security
Network architectures
Information security
Defense and security
Modeling
Systems modeling
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top