Bibliographic Details
| Title: |
Time-Based Moving Target Defense Using Bayesian Attack Graph Analysis |
| Authors: |
Hyejin Kim, Euiseok Hwang, Dongseong Kim, Jin-Hee Cho, Terrence J. Moore, Frederica F. Nelson, Hyuk Lim |
| Source: |
IEEE Access, Vol 11, Pp 40511-40524 (2023) |
| Publisher Information: |
IEEE, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| Subject Terms: |
Moving target defense, Bayesian attack graph, software-defined networking, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| More Details: |
The moving target defense (MTD) is a proactive cybersecurity defense technique that constantly changes potentially vulnerable points to be attacked, to confuse the attackers, making it difficult for attackers to infer the system configuration and nullify reconnaissance activities to a victim system. We consider an MTD strategy for software-defined networking (SDN) environment where every SDN switch is controlled by a central SDN controller. As the MTD may incur excessive usage of the network/system resources for cybersecurity purposes, we propose to perform the MTD operations adaptively according to the security risk assessment based on a Bayesian attack graph (BAG) analysis. For accurate BAG analysis, we model random and weakest-first attack behaviors and incorporate the derived analytical models into the BAG analysis. Using the BAG analysis result, we formulate a knapsack problem to determine the optimal set of vulnerabilities to be reconfigured under a constraint of SDN reconfiguration overhead. The experiment results prove that the proposed MTD strategy outperforms the full MTD and random MTD counterparts in terms of the maximum/average of attack success probabilities and the number of SDN reconfiguration updates. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/10106238/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3269018 |
| Access URL: |
https://doaj.org/article/155d995105f84a90b5dc21fc48c290b2 |
| Accession Number: |
edsdoj.155d995105f84a90b5dc21fc48c290b2 |
| Database: |
Directory of Open Access Journals |
