.png){kind=icon}
.png){kind=icon}
Return
Job Completion Time Under Migration-Based Dynamic Platform Technique
.png){kind=icon}
PRE
AI
DOI：10.1109/TSC.2020.2989215.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Security
Measurement
Analytical models
Numerical models
Virtual machine monitors
Markov processes
Analytical modeling
job completion time
migration-based dynamic platform technique
moving target defense
quantitative evaluation
stochastic reward net
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.8
Papers:
2.1K
Citations:
6.5K