Regulation in Cyberspace

Abstract

Regulation in cyberspace is an emerging challenge. It is a complex and dynamic domain that is largely driven by the business-civilian sector and has the potential to cause significant damage to national security. This essay surveys the unique characteristics of cyberspace and the various strategies adopted in other countries in order to manage cyber risk. It proposes a multilayered regulatory model together with concrete recommendations for the regulation of the business-civilian sector in cyberspace. The resilience of the private sector in cyberspace is directly related to national security. The private sector usually constitutes the weak point where a cyber-attack develops. Nonetheless, the survey of regulation in cyberspace in Western countries, including Israel, points to the lack of an appropriate response to this weakness. This essay attempts to fill that gap and, in order to do so, it makes use of the regulatory principles used by other countries— the United States, Britain, France, Germany, and the European Union—and also learns from other regulated domains, namely environmental protection and nuclear energy. National approaches, the variety of regulatory tools, and the systems of incentives used in the attempts to regulate cyberspace worldwide, together with models for collaboration between the public and private sectors and state compensation mechanisms that were observed in environmental protection and nuclear energy domains, have contributed to the development of an innovative regulatory model for cyberspace in the business-civilian sector in Israel.

Enhancing corrosion-resistant alloy design through natural language processing and deep learning

“…After training, optimization within the input parameter space of both the models was done to maximize the pitting potential using the multidimensional gradient descent algorithm (62), using a learning rate of 0.0001 in all cases. The augmented Keras model class, capable of returning the derivative of the output with respect to the inputs (AugNet) (31,63) has been used for this purpose with both the models. In case of the process-aware DNN, gradients were calculated only with respect to the numerical inputs.…”