Title | Job Completion Time Under Migration-Based Dynamic Platform Technique |
Publication Type | Journal Article |
Year of Publication | 2022 |
Authors | X Chang, Y Shi, Z Zhang, Z xu, and KS Trivedi |
Journal | IEEE Transactions on Services Computing |
Volume | 15 |
Issue | 3 |
Start Page | 1345 |
Pagination | 1345 - 1357 |
Date Published | 01/2022 |
Abstract | Migration-based Dynamic Platform (MDP) technique, a type of Moving Target Defense (MTD) techniques, defends against sophisticated cyber-attacks by randomly and dynamically selecting a platform for executing service/job. Security defense mechanisms protect service/job usually at the cost of degrading its performance. Therefore, it is valuable to make a trade-off between service/job security and its performance. However, previous researches on MTD techniques either focused on analyzing MTD effectiveness of protecting service/job or studied service/job performance with the assumption that attacks on service/job make no influence on its execution. This article aims to apply analytical modeling techniques to investigate the impact of MDP technique on job completion time in a system under attack. We use Stochastic Reward Nets (SRNs) to develop a Markov chain-based model for capturing typical behaviors of the adversary, the vulnerable system and a job. The formulas are derived for calculating the metrics of interest. Numerical analysis is conducted to study the impact of key parameters on job completion time and job security loss. |
DOI | 10.1109/TSC.2020.2989215 |
Short Title | IEEE Transactions on Services Computing |