Abstract
People-Readable Threat Intelligence (PRTI) recommender Systems aim to address the problem of information explosion of PRTIs and make personalized recommendation for users. In general, PRTI is highly condensed, and consists of security items, network entities and emerging hacker organizations, attacks, etc. PRTI may also contain many Machine-Readable Threat Intelligence (MRTI). However, existing methods are unaware of such external knowledge and cannot fully discover latent knowledge-level connections among PRTIs. Under this scenario, the existing generic knowledge graphs will introduce too much noise and can not consider the entity relationship in terms of the attack chain. To solve the problems above, in this paper, we propose a knowledge graph for People-Readable Threat Intelligence recommendation (PRTIRG) and incorporates knowledge graph representation into PRTI recommender system for click-through prediction. The key components of PRTIRG are the denoising entity extraction module and the knowledge-aware long short-term memory neural network (KLSTM). Through extensive experiments on real-world datasets, we demonstrate that the PRTIRG is more effective and accurate than baselines.
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Acknowledgment
This work is supported by the Key Research Program of Beijing Municipal Science & Technology Commission (Grant No. D18110100060000, D181100000618003), the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDC02040100, XDC02030200, XDC02020200), the National Key Research and Development Program of China (Grant No. 2017YFC08218042, 2018YFB0803602, 2016QY06X1204). This research was also partially supported by Key Laboratory of Network Assessment Technology, Chinese Academy of Sciences and Beijing Key Laboratory of Network Security and Protection Technology.
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Du, M., Jiang, J., Jiang, Z., Lu, Z., Du, X. (2019). PRTIRG: A Knowledge Graph for People-Readable Threat Intelligence Recommendation. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_5
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