具有随机参数矩阵和时间相关噪声的不可靠网络:在欺骗攻击下分布式估计
Raquel Caballero-Águila1, María J García-Ligero2, Aurora Hermoso-Carazo2
1Departamento de Estadística e I.O., Universidad de Jaén, Campus Las Lagunillas, 23071 Jaén, Spain.
Mathematical biosciences and engineering : MBE
|September 7, 2023
概括
本研究为面临随机参数,噪音和欺骗攻击的网络系统提出了新的分布式估计算法. 这些方法在复杂,不确定的环境中提高了准确性.
科学领域:
- 控制系统和信号处理系统
- 网络系统分析 网络系统分析
- 估计理论估计理论
背景情况:
- 网络系统容易受到各种不确定性的影响,包括随机参数,相关噪音和欺骗攻击.
- 准确的状态估计对于这些系统的可靠运行至关重要.
- 现有的方法往往难以同时解决多个网络诱导的现象.
研究的目的:
- 为网络系统开发强大的分布式过和固定点光滑算法.
- 为在不完整的信息和各种不确定性下提供统一的估计框架.
- 为了应对随机参数矩阵,时间相关的附加噪声和随机欺骗攻击所带来的挑战.
主要方法:
- 提出了一个两阶段的分布式估计算法.
- 第一个阶段使用本地和相邻节点数据生成中间估计器.
- 第二阶段通过最小平方矩阵加权的线性组合结合中间估计器,使用基于共变的技术而不需要信号演变模型.
主要成果:
- 开发的算法有效地处理随机参数矩阵,时间相关的附加噪音和随机欺骗攻击.
- 建立了一个统一的框架,用于在信息不完整的系统中分布式估计.
- 数字实验验证算法的适用性和有效性,证明不确定性和攻击对估计准确性的影响.
结论:
- 提出的分布式估计算法为复杂的不确定性下网络系统提供了强大的解决方案.
- 基于共差的方法通过不需要信号演变模型来提供灵活性.
- 该研究强调,在存在网络漏洞时,迫切需要强大的估计策略.
相关概念视频
Random Error
923
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
923
Distributions to Estimate Population Parameter
4.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.1K
Propagation of Uncertainty from Random Error
724
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...
724
Estimating Population Mean with Unknown Standard Deviation
8.0K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
William S. Gosset (1876–1937) of the...
8.0K
Propagation of Uncertainty from Systematic Error
552
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...
552
Random and Systematic Errors
11.0K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
11.0K


