Impact of Information based Classification on Network Epidemics.
Impact of Information based Classification on Network Epidemics.
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DOI：
10.1038/srep28289
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发表时间：
2016-06-22
影响因子：
4.6
通讯作者：
Sinha DN
中科院分区：
文献类型：
--
作者：
Mishra BK;Haldar K;Sinha DN
制定数学模型来准确近似网络中的恶意传播是一个困难的过程，因为我们固有地缺乏对本质上表征更广泛情况的几个底层物理过程的理解。本文的目的是了解可用信息对控制恶意网络流行病的影响。提出了 1-n-n-1 型差分流行病模型，其中差分允许基于症状的分类。这是首次尝试将这种分类添加到现有的流行病框架中。该模型被纳入称为 DifEpGoss 架构的五类系统中。分析揭示了流行阈值，并根据该阈值分析系统的长期行为。在这项工作中，使用了分别具有 22002、22469 和 22607 个无向边的三个真实网络数据集。数据集表明，模型中给出的基于分类的预防对于遏制网络流行病具有良好的作用。进一步的基于模拟的实验采用了攻击和防御强度的三类分类，这使我们能够考虑 27 种不同的可能性。这些实验进一步证实了所提出模型的实用性。论文最后得出了几个有趣的结果。
Formulating mathematical models for accurate approximation of malicious propagation in a network is a difficult process because of our inherent lack of understanding of several underlying physical processes that intrinsically characterize the broader picture. The aim of this paper is to understand the impact of available information in the control of malicious network epidemics. A 1-n-n-1 type differential epidemic model is proposed, where the differentiality allows a symptom based classification. This is the first such attempt to add such a classification into the existing epidemic framework. The model is incorporated into a five class system called the DifEpGoss architecture. Analysis reveals an epidemic threshold, based on which the long-term behavior of the system is analyzed. In this work three real network datasets with 22002, 22469 and 22607 undirected edges respectively, are used. The datasets show that classification based prevention given in the model can have a good role in containing network epidemics. Further simulation based experiments are used with a three category classification of attack and defense strengths, which allows us to consider 27 different possibilities. These experiments further corroborate the utility of the proposed model. The paper concludes with several interesting results.