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Table 3 Comparison between word embedding and one-hot representation

From: Using deep learning to solve computer security challenges: a survey

Method FP 1 FN 2 Precision Recall F1-measure
Word Embedding 3 680 219 96.069% 98.699% 97.366%
One-hot Vector 4 711 705 95.779% 95.813% 95.796%
DeepLog 5 833 619 95% 96% 96%
  1. 1FP: false positive; 2FN: False negative; 3Word Embedding: Log keys are embedded by Continuous Bag of words; 4 One-hot Vector: We reproduced the results according to DeepLog; 5 DeepLog: Orignial results presented in the paper (Du et al. 2017).