Skip to main content
Cybersecurity

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).
Back to article page