Browsing by Subject "Cybersecurity"

Event data provide high-resolution and high-volume information about political events. From COPDAB to KEDS, GDELT, ICEWS, and PHOENIX, event datasets and the frameworks that produce them have supported a variety of research efforts across fields and including political science. While these datasets are machine-coded from vast amounts of raw text input, they nonetheless require substantial human effort to produce and update sets of required dictionaries. I introduce a novel method for generating large dictionaries appropriate for event-coding given only a small sample dictionary. This technique leverages recent advances in natural language processing and deep learning to greatly reduce the researcher-hours required to go from defining a new domain-of-interest to producing structured event data that describes that domain. An application to cybersecurity is described and both the generated dictionaries and resultant event data are examined. The cybersecurity event data are also examined in relation to existing datasets in related domains.

Scholars have long recognized and debated the effects of the "security dilemma," where efforts by states to enhance their security can decrease the security of others. The severity of a security dilemma, and the prospects for cooperation under the dilemma, are greatly affected by military technology. In this article, I apply the security dilemma framework to a revolutionary new form of conflict: cyberwarfare. I argue that cooperation over cyberwarfare is made challenging due to the security dilemma, and that the unique characteristics of cyberwarfare make it difficult to break out of this dilemma. The reluctance and failure of states to achieve cooperation over cyberwarfare likely reflects, in part, the constraints of this "cybersecurity dilemma." Some states have strong incentives, however, to promote limitations on offensive cyberwarfare. Thus, I propose ways in which cooperation may eventually be achieved despite these challenges.