Insights in the field of cybersecurity economics empower decision makers to make informed decisions that improve their evaluation and management of situations that may lead to catastrophic consequences and threaten the sustainability of digital ecosystems. By drawing on these insights, cybersecurity practitioners have been able to respond to many complex problems that have emerged within the context of cybersecurity over the last two decades. The academic field of cybersecurity economics is highly interdisciplinary since it combines core findings and tools from disciplines such as sociology, psychology, law, political science, and computer science. This study aims to develop an extensive and consistent survey based on a literature review and publicly available reports. This review contributes by aggregating the available knowledge from 28 studies, out of a collection of 628 scholarly articles, to answer five specific research questions. The focus is how identified topics have been conceptualized and studied variously. This review shows that most of the cybersecurity economics models are transitioning from unrealistic, unverifiable, or highly simplified fundamental premises toward dynamic, stochastic, and generalizable models.

The multisided platform (MSP) is an essential business construct in the digital economy. Some of the largest companies in the world—including Google, Amazon, and eBay—exploit the MSP in their business models. Fundamental insights into the MSP are crucial to understand the business operations of the digital economy and how new innovative digital services are adopted in the market. The MSP ecosystem is complex and dynamic, and involves heterogeneous stakeholders with different business motivations. This paper classifies the various types of MSPs, distinguished by the network effect between user groups. Moreover, this paper shows how the original diffusion model of Frank Bass can be extended to analyze the temporal evolution of multisided platforms. Analytical models using coupled sets of ordinary differential equations are developed for several examples of two-sided platforms. For some of these examples, analytical solutions are found.

In bufferless optical burst/packet switched (OBS/OPS) networks, data (bursts or packets) may be lost due to contention or equipment failure. Diversity coding-based path protection schemes can be used to protect data from a single-trunk-failure with potentially more efficient resource utilization compared to dedicated (e.g. 1+1) path protection schemes and help reduce the burst/packet loss ratio. This paper provides a scalable and accurate burst/packet loss ratio approximation based on the Erlang Fixed-Point Approximation for networks that employ protection based on diversity coding and for networks that employ protection based on both diversity coding and 1+1 path protection. We use discrete event simulations to assess the accuracy of the approximation based on a wide range of and scenarios in a 10-node circular lattice and NSFNet networks. Further, we discuss the effect of parameter settings and the effect of the choice of the wavelength selection method on the accuracy of the approximation. We consider scenarios without wavelength conversion and with full wavelength conversion and two types of users: premium and regular. The premium users, typically of mission critical services, receive protection while the regular users do not. We compare diversity coding and 1+1 path protection in a 10-node circular lattice network. The results show that for this example network, for the premium users and under low traffic load, the burst/packet loss ratio in OBS/OPS networks is lower for diversity coding compared to 1+1 path protection. However, under heavy traffic load, we observe a lower loss ratio for the case of 1+1 path protection.