一种可解释的自由文本击键事件序列分类模型

TypeNet是一种基于两层长短时记忆网(LSTM)分支结构的孪生网络模型，在自由文本击键事件序列分类任务上取得了很好的效果，但缺乏可解释性。为此，该文改进了TypeNet模型，提出一种基于单层LSTM分支结构的孪生网络模型TypeNet II。TypeNet II模型用多层感知机度量两个分支输出表征向量差的绝对值体现的特征序列的相似度。模型训练完毕后，用多元二项式回归模拟多层感知机部分，基于得到的多元二项式对模型进行解释。实验结果表明，TypeNet II模型的分类效果超出了已有的TypeNet模型，多元二项式回归的结果具有泛化性，表征向量差的绝对值与相似度量之间存在非线性关系。

An Interpretable Free-text Keystroke Event Sequence Classification Model

TypeNet is a Siamese network model based on two-layer Long-Short Term Memory (LSTM) branch structure. It has achieved good results in the classification of free-text keystroke event sequences, but lacks interpretation. Therefore, the TypeNet model is transformed, and a Siamese network TypeNet II based on a single-layer LSTM branch structure is proposed. A multi-layer perceptron is used to measure the similarity of two feature sequences reflected by the absolute value of the difference between the output embeddings of the two branches. After the model training, the multi-layer perceptron is simulated by a multivariate binomial expression. Based on the obtained multivariate binomial expression, the classification judgment of the model can be explained. The experimental results show that the classification effect of the TypeNet II model exceeds the existing TypeNet model. The results of multivariate binomial regression are generalized, and there is a nonlinear relationship between the absolute value of the difference of the embeddings and the similarity measure.