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This paper proposed a Meta learning classification method based on few-shot samples, called BiLSTM-Ridge regression model, which can recognize and learn the characteristics of high threat log samples through less sample learning, and has better generalization detection ability in new unknown test tasks.
we design a Meta knowledge learner in the Meta training scenario to extract the sample features of high threat logs and improve the adaptability of Meta learners in new tasks. We prove that our method achieves good results on the 5-way 1-shot and 5-way 3-shot problems on the real 335 high threat log data sets.
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IEEE/ICACT20220392 Slide.16
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