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Threat intelligence sharing has become a growing concept, whereby entitiescan exchange patterns of threats with each other, in the form of indicators, toa community of trust for threat analysis and incident response. However,sharing threat-related information have posed various risks to an organizationthat pertains to its security, privacy, and competitiveness. Given thecoinciding benefits and risks of threat information sharing, some entities haveadopted an elusive behavior of "free-riding" so that they can acquire thebenefits of sharing without contributing much to the community. So far,understanding the effectiveness of sharing has been viewed from the perspectiveof the amount of information exchanged as opposed to its quality. In thispaper, we introduce the notion of quality of indicators (\qoi) for theassessment of the level of contribution by participants in information sharingfor threat intelligence. We exemplify this notion through various metrics,including correctness, relevance, utility, and uniqueness of indicators. Inorder to realize the notion of \qoi, we conducted an empirical study and takena benchmark approach to define quality metrics, then we obtained a referencedataset and utilized tools from the machine learning literature for qualityassessment. We compared these results against a model that only considers thevolume of information as a metric for contribution, and unveiled variousinteresting observations, including the ability to spot low qualitycontributions that are synonym to free riding in threat information sharing.

Beyond Free Riding: Quality of Indicators for Assessing Participation in Information Sharing for Threat Intelligence