Related Experiment Video
Updated: Aug 16, 2025
04:52
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
Published on: February 3, 2023
1.0K
Population processes in cyber system variability
Marc Mangel1,2,3, Alan Brown4
1Department of Applied Mathematics, University of California, Santa Cruz, Santa Cruz, CA, United States of America.
Plos One
|December 27, 2022
Summary
This study applies population biology models to cyber systems, analyzing component functionality and operating system (OS) updates using Markov processes and simulations. Findings offer new insights into cyber system variability and performance scaling.
Area of Science:
- Cyber-physical systems
- Stochastic modeling
- Systems biology
Background:
- Cyber systems exhibit inherent variability, impacting reliability and performance.
- Understanding this variability is crucial for designing robust and scalable systems.
- Traditional approaches may not fully capture the dynamic nature of cyber component states and updates.
Purpose of the Study:
- To apply stochastic population biology concepts to model cyber system variability.
- To analyze the functional states of cyber components and the dynamics of operating system (OS) updates.
- To develop scalable methods for assessing the performance and security of large cyber systems.
Main Methods:
- Modeled component state transitions (functional/nonfunctional) as a Markov process.
- Derived equations for component functionality probability and system performance metrics.
- Utilized stochastic simulation and solved the forward Kolmogorov (Fokker-Planck) equation.
- Developed a Gaussian approximation for large-scale system analysis.
- Modeled OS update schedules and transition probabilities, incorporating OS compromise.
Main Results:
- Derived an equation for individual cyber component functionality probability.
- Developed a performance metric dependent on functional and nonfunctional component counts.
- Obtained a Gaussian approximation for system properties, enabling scalability to large systems.
- Modeled OS update dynamics and derived the forward equation for OS status over time.
- Quantified the probability of a cyber component having an unexploited OS.
Conclusions:
- An interdisciplinary approach, integrating population biology with cyber systems, provides novel perspectives.
- Stochastic modeling offers powerful tools for understanding and predicting cyber system behavior.
- The developed methods allow for the analysis and scaling of complex cyber systems with inherent variability.
More Related Videos
12:52High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression
Published on: April 18, 2021
4.9K
10:44Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
Published on: December 7, 2021
2.2K
Related Concept Videos
01:09Mutation, Gene Flow, and Genetic Drift
59.1K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
59.1K
01:19Cyclic Processes And Isolated Systems
2.8K
A thermodynamic system with zero heat exchange and work is an isolated system. For these systems, the internal energy remains constant.
In the case of a non-isolated system, the change in the internal energy is zero only if the process is cyclic. A thermodynamic process is considered cyclic if the system undergoes a series of changes and returns to its initial state.
Consider a cyclic process that returns to its initial state, undergoing a four-step process. The heat transfer along each...
In the case of a non-isolated system, the change in the internal energy is zero only if the process is cyclic. A thermodynamic process is considered cyclic if the system undergoes a series of changes and returns to its initial state.
Consider a cyclic process that returns to its initial state, undergoing a four-step process. The heat transfer along each...
2.8K
01:10Propagation of Uncertainty from Systematic Error
591
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
591
00:59Propagation of Uncertainty from Random Error
769
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
769
01:31Classification of Systems-II
202
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
202
01:11Variability: Analysis
173
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
The range is a simple measure of variability, indicating the difference between the highest and...
173