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