网络安全投资的局部平均场分析
A Local Mean Field Analysis of Security Investments in Networks
摘要：
让互联网和一般网络中的代理人投资和部署安全功能和协议是一项挑战，特别是因为网络外部性的存在所产生的经济原因。我们在本文中的目标是仔细建模和量化的影响，这种外部性的投资和部署，在网络中的安全功能和协议。具体来说，我们研究了一个网络的相互关联的代理，这是受流行病的风险，如传播病毒和蠕虫引起的，并可以决定是否投资一些金额自我保护和部署安全解决方案。我们在论文中做了三个贡献。首先，我们介绍了一个通用的模型，它结合了流行病传播模型与经济模型的代理人，捕捉网络效应和外部性。其次，借用统计物理学中的思想和技术，我们引入了局部平均场（LMF）模型，该模型扩展了标准平均场近似，以考虑局部邻域的相关结构。第三，我们解决了LMF模型在网络中的外部性，我们推导出稀疏随机图的解析解，我们得到的渐近结果。我们明确确定网络外部性对投资和部署安全功能的决策的影响。换句话说，我们确定了决定采用安全技术的经济和网络属性。
英文摘要：
Getting agents in the Internet, and in networks in general, to invest in and deploy security features and protocols is a challenge, in particular because of economic reasons arising from the presence of network externalities. Our goal in this paper is to carefully model and quantify the impact of such externalities on the investment in, and deployment of, security features and protocols in a network. Specifically, we study a network of interconnected agents, which are subject to epidemic risks such as those caused by propagating viruses and worms, and which can decide whether or not to invest some amount to self-protect and deploy security solutions. We make three contributions in the paper. First, we introduce a general model which combines an epidemic propagation model with an economic model for agents which captures network effects and externalities. Second, borrowing ideas and techniques used in statistical physics, we introduce a Local Mean Field (LMF) model, which extends the standard mean-field approximation to take into account the correlation structure on local neighborhoods. Third, we solve the LMF model in a network with externalities, and we derive analytic solutions for sparse random graphs, for which we obtain asymptotic results. We explicitly identify the impact of network externalities on the decision to invest in and deploy security features. In other words, we identify both the economic and network properties that determine the adoption of security technologies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
