论文标题
网络室类型的隔室模型
A compartmental model for cyber-epidemics
论文作者
论文摘要
在我们越来越相互联系的世界中,特定的风险是偶然或故意产生的网络流行病(或网络流行病),在这种情况下,网络病毒从设备到设备传播到使全球互联网系统促进与基本服务关闭相关的经济成本和社会危害的毁灭性后果。我们介绍了一个隔间模型,用于研究恶意软件的传播以及通过在同一图形结构(全球连接设备的全局网络)上发展的不同波的发病率的意识。这是通过考虑由两个组件制成的矢量隔室来实现的,第一个是对新恶意软件传播的设备状态的描述,第二个是对设备用户对存在网络威胁的认识的认识。通过在此类隔室之间引入合适的过渡速率,然后可以跟随网络流动性的演变,从网络中播种新病毒的那一刻,直到给定用户意识到他/她的设备遭受了损害,因此启动了一波意识,最终以适当的抗病毒软件的发展而结束。然后,我们比较了恶意软件能够在ERDőS-Rényi和无尺度网络体系结构中产生的整体损害,以使病毒对每种设备造成固定损害以及该病毒在从设备上复制时进行突变的情况。我们的结果实际上构成了构建特定隔室模型的尝试,该模型的变量和参数是完全定制的,用于描述网络流行学。
In our more and more interconnected world, a specific risk is that of a cyber-epidemic (or cyber-pandemic), produced either accidentally or intentionally, where a cyber virus propagates from device to device up to undermining the global Internet system with devastating consequences in terms of economic costs and societal harms related to the shutdown of essential services. We introduce a compartmental model for studying the spreading of a malware and of the awareness of its incidence through different waves which are evolving on top of the same graph structure (the global network of connected devices). This is realized by considering vectorial compartments made of two components, the first being descriptive of the state of the device with respect to the new malware's propagation, and the second accounting for the awareness of the device's user about the presence of the cyber threat. By introducing suitable transition rates between such compartments, one can then follow the evolution of a cyber-epidemic from the moment at which a new virus is seeded in the network, up to when a given user realizes that his/her device has suffered a damage and consequently starts a wave of awareness which eventually ends up with the development of a proper antivirus software. We then compare the overall damage that a malware is able to produce in Erdős-Rényi and scale-free network architectures for both the case in which the virus is causing a fixed damage on each device and the case where, instead, the virus is engineered to mutate while replicating from device to device. Our result constitute actually the attempt to build a specific compartmental model whose variables and parameters are entirely customized for describing cyber-epidemics.