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- W3026270031 abstract "With the rapid development of open source threat intelligence, researchers are sharing threat-related articles through blog articles or reports. The shared information, like IoC and threat action, can promote defense ability against the potential threats. However, with high columns of threat-related articles published, extracting threat-related information from unstructured context, especially threat action, has become a challenge. To overcome this problem, we present a method to extract threat action from threat-related articles. Firstly, topics are indexed with latent semantic indexing method. Next, with ATT&CK as taxonomy, semantic similarities are computed as classification features. Finally, a multi-label classification model extracts threat actions from threat-related articles. To evaluate our method, a labelled APT group-related dataset is collected and shared. The result shows, the maximum precision, recall ratio and F-1 measure for multi-label classification are 59.50%, 69.86% and 56.96%. Our method can help researcher understand network situation and make proactive defense measures against potential threats." @default.
- W3026270031 created "2020-05-29" @default.
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- W3026270031 date "2019-11-01" @default.
- W3026270031 modified "2023-10-14" @default.
- W3026270031 title "Extraction of Threat Actions from Threat-related Articles using Multi-Label Machine Learning Classification Method" @default.
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- W3026270031 doi "https://doi.org/10.1109/iicspi48186.2019.9095885" @default.
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