| Title | Graph Mining-based Trust Evaluation Mechanism with Multidimensional Features for Large-scale Heterogeneous Threat Intelligence |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Gao, Y., Li, X., Li, J., Gao, Y., Guo, N. |
| Conference Name | 2018 IEEE International Conference on Big Data (Big Data) |
| Date Published | dec |
| Keywords | cyber threat intelligence sharing platforms, data mining, data protection, Databases, feature extraction, graph mining, graph theory, GTTI, heterogeneous threat intelligence, Human Behavior, human trust, intelligence feature extraction, IP networks, Malware, Organizations, pubcrawl, security, security of data, Standards, TISP, trust evaluation, trust evaluation mechanism, trust-aware intelligence architecture model, trust-aware threat intelligence sharing platform, Trusted Computing |
| Abstract | More and more organizations and individuals start to pay attention to real-time threat intelligence to protect themselves from the complicated, organized, persistent and weaponized cyber attacks. However, most users worry about the trustworthiness of threat intelligence provided by TISPs (Threat Intelligence Sharing Platforms). The trust evaluation mechanism has become a hot topic in applications of TISPs. However, most current TISPs do not present any practical solution for trust evaluation of threat intelligence itself. In this paper, we propose a graph mining-based trust evaluation mechanism with multidimensional features for large-scale heterogeneous threat intelligence. This mechanism provides a feasible scheme and achieves the task of trust evaluation for TISP, through the integration of a trust-aware intelligence architecture model, a graph mining-based intelligence feature extraction method, and an automatic and interpretable trust evaluation algorithm. We implement this trust evaluation mechanism in a practical TISP (called GTTI), and evaluate the performance of our system on a real-world dataset from three popular cyber threat intelligence sharing platforms. Experimental results show that our mechanism can achieve 92.83% precision and 93.84% recall in trust evaluation. To the best of our knowledge, this work is the first to evaluate the trust level of heterogeneous threat intelligence automatically from the perspective of graph mining with multidimensional features including source, content, time, and feedback. Our work is beneficial to provide assistance on intelligence quality for the decision-making of human analysts, build a trust-aware threat intelligence sharing platform, and enhance the availability of heterogeneous threat intelligence to protect organizations against cyberspace attacks effectively. |
| DOI | 10.1109/BigData.2018.8622111 |
| Citation Key | gao_graph_2018 |
Graph Mining-based Trust Evaluation Mechanism with Multidimensional Features for Large-scale Heterogeneous Threat Intelligence