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Identification and extraction of Tactics, Techniques and Procedures (TTPs) for Cyber Threat Intelligence (CTI) restore the full picture of cyber attacks and guide the analysts to assess the system risk. Existing frameworks can hardly provide uniform and complete processing mechanisms for TTPs information extraction without adequate knowledge background. A multi-instance learning approach named SeqMask is proposed in this paper as a solution. SeqMask extracts behavior keywords from CTI evaluated by the semantic impact, and predicts TTPs labels by conditional probabilities. Still, the framework has two mechanisms to determine the validity of keywords. One using expert experience verification. The other verifies the distortion of the classification effect by blocking existing keywords. In the experiments, SeqMask reached 86.07% and 73.99% in F1 scores for TTPs classifications. For the top 20% of keywords, the expert approval rating is 92.20%, where the average repetition of keywords whose scores between 100% and 90% is 60.02%. Particularly, when the top 65% of the keywords were blocked, the F1 decreased to about 50%; when removing the top 50%, the F1 was under 31%. Further, we also validate the possibility of extracting TTPs from full-size CTI and malware whose F1 are improved by 2.16% and 0.81%. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to determine the application value of a trauma index (TI) to assess condition and likelihood of death in hospitalized patients with acute trauma (AT)., Methods: Trauma index scores and injury severity scores (ISS) were assessed in 1,802 randomly selected cases of AT-hospitalized patients. The receiver operating characteristic (ROC) curve was used to compare the clinical values of TI and ISS values to predict outcomes in AT-hospitalized patients., Results: The area under the ROC curve for TI scores was 0.896 (95% CI [0.881, 0.909]), while for ISS, it was 0.792 (95% CI [0.773, 0.811]). This difference was not statistically significant (z = 3.236, P = 0.001). Potentially critical disease conditions in AT-hospitalized patients were best identified when TI scores were ≥ 16 points and ISS values were ≥ 22 points., Conclusions: Trauma Index scores exhibited a higher resolution for outcome prediction in AT-hospitalized patients compared to ISS values. The implementation of this scale was simple, reliable, easy to learn, and could quickly identify disease, which is vital for early detection and treatment of critical trauma patients.