Advanced Persistent Threat (APT) having focused target along with advanced and persistent attacking skills under great concealment is a new trend followed for cyber-attacks. Threat intelligence helps in detecting and preventing APT by collecting a host of data and analyzing malicious behavior through efficient data sharing and guaranteeing the safety and quality of information exchange. For better protection, controlled access to intelligence information and a grading standard to revise the criteria in diagnosis for a security breach is needed. This paper analyses a threat intelligence sharing community model and proposes an improvement to increase the efficiency of sharing by rethinking the size and composition of a sharing community. Based on various external environment variables, it filters the low-quality shared intelligence by grading the trust level of a community member and the quality of a piece of intelligence. We hope that this research can fill in some security gaps to help organizations make a better decision in handling the ever-increasing and continually changing cyber-attacks.
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