分析复杂网络的稳定性与攻击成功率
1State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China.
Entropy (Basel, Switzerland)
|November 24, 2023
概括
这项研究引入了一个新的网络稳定性衡量标准,稳定性-ASR (RASR),用于计算攻击成功率. 一个并行算法,PRQMC,有效地计算大型网络的RASR,改进了现有的方法.
科学领域:
- 网络科学 网络科学
- 复杂系统分析 复杂系统分析
- 计算数学 计算数学 计算数学
背景情况:
- 网络稳定性分析对于了解网络应对故障和攻击的弹性至关重要.
- 现有的强度指标往往忽略了攻击成功率 (ASR) 变量,假设攻击总是成功.
- 现实世界的网络攻击表现出不同的成功概率,需要更现实的评估方法.
研究的目的:
- 提出一种新的网络稳定性测量方法,即稳定性-ASR (RASR),它结合了个别节点的攻击成功率 (ASR).
- 开发一种高效的并行算法 (PRQMC),用于在大规模复杂网络中计算RASR.
- 引入一种新的攻击策略 (HBnnsAGP) 来评估网络RASR的下界.
主要方法:
- 使用数学预期来定义强度-ASR (RASR) 度量.
- 采用随机准蒙特卡洛 (RQMC) 集成来实现高效准确的RASR近似.
- 开发一个并行算法 (PRQMC) 以加快RASR计算在大型网络.
- 介绍HBnnsAGP攻击策略,以建立网络RASR的下限.
主要成果:
- 拟议的RASR措施有效量化了在可变攻击成功概率下网络的稳定性.
- 在大型网络的RASR计算中,PRQMC算法显示了显著的效率增长.
- 在六个现实世界网络上的实验验证证证了RASR和PRQMC与现有方法相比的有效性.
- 该HBnnsAGP战略为网络稳定性评估提供了更严格的下限.
结论:
- 新的RASR指标通过考虑ASR提供了对网络稳健性的更现实的评估.
- PRQMC算法为分析大型复杂网络提供了可扩展和高效的解决方案.
- 该研究通过引入强度评估和攻击策略评估的改进方法来推进网络科学.
相关概念视频
Protein Networks
4.0K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.0K
Confidence Coefficient
7.6K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
7.6K
Hazard Rate
114
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
114
Protein-protein Interfaces
12.5K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
12.5K
Probability in Statistics
13.2K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
13.2K
Assumptions of Survival Analysis
135
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
135


