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一种可解释的自由文本击键事件序列分类模型

张畅 韩继红 张玉臣 李福林

张畅, 韩继红, 张玉臣, 李福林. 一种可解释的自由文本击键事件序列分类模型[J]. 电子与信息学报, 2023, 45(2): 698-706. doi: 10.11999/JEIT211567
引用本文: 张畅, 韩继红, 张玉臣, 李福林. 一种可解释的自由文本击键事件序列分类模型[J]. 电子与信息学报, 2023, 45(2): 698-706. doi: 10.11999/JEIT211567
ZHANG Chang, HAN Jihong, ZHANG Yuchen, LI Fulin. An Interpretable Free-text Keystroke Event Sequence Classification Model[J]. Journal of Electronics & Information Technology, 2023, 45(2): 698-706. doi: 10.11999/JEIT211567
Citation: ZHANG Chang, HAN Jihong, ZHANG Yuchen, LI Fulin. An Interpretable Free-text Keystroke Event Sequence Classification Model[J]. Journal of Electronics & Information Technology, 2023, 45(2): 698-706. doi: 10.11999/JEIT211567

一种可解释的自由文本击键事件序列分类模型

doi: 10.11999/JEIT211567
详细信息
    作者简介:

    张畅:男,讲师,研究方向为网络安全、事件序列分类

    韩继红:女,教授,研究方向为网络安全、安全协议分析

    张玉臣:男,教授,研究方向为网络安全

    李福林:男,副教授,研究方向为网络安全

    通讯作者:

    张畅 zhang_chang_xd@163.com

  • 中图分类号: TP181

An Interpretable Free-text Keystroke Event Sequence Classification Model

  • 摘要: 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。
  • 图  1  TypeNet II的结构

    图  2  决策层的多元多项式回归模型

    图  3  TypeNet II模型的训练和验证准确率

    图  4  分类效果随负样本类数量的变化情况

    图  5  表征向量维度为128的TypeNet II模型得到比较层的数值的小提琴图

    图  6  表征向量维度为64的TypeNet II模型得到比较层的数值的小提琴图

    图  8  被试者179773的特征序列对应的比较层和模型输出的可视化

    图  7  被试者175380的特征序列对应的比较层和模型输出的可视化

    表  1  TypeNet II模型的主要超参数

    LSTM层
    神经元数
    LSTM隐藏层间
    dropout比率
    LSTM与分支的输出Dense
    层间dropout比率
    分支的输出Dense层的神经元数和
    决策层输入Dense层的神经元数
    优化器初始
    学习率
    批大小
    参数值1280.20.5取集合{128,64,32,3,2}中的值Nadam0.001512
    下载: 导出CSV

    表  2  TypeNet II模型不同表征向量维度对应的最佳训练验证准确率

    128643232
    验证准确率(%)95.7993.9180.0882.4382.24
    下载: 导出CSV

    表  3  模型的分类效果

    TypeNet: contrastive lossTypeNet:
    triplest loss
    TypeNet:
    SM-CL, G=6
    TypeNet:
    SM-TL, G=6
    TypeNet II:
    表征向量为128维
    TypeNet II:
    表征向量为64维
    等错误率(%)5.4[13]2.2[13]2.42[13]1.85[13]1.762.12
    下载: 导出CSV

    表  4  两个表征向量维度下$ {P_{{\text{tr}}}} $上多元多项式$ f $不同自由度对应的$ {R^{\text{2}}} $

    12864
    自由度123123
    训练$ {R^2} $0.8200920.915693–0.5478540.8200320.919382–0.566558
    下载: 导出CSV

    表  5  两个表征向量维度下$ {P_{{\text{tr}}}} $上多元2阶项式岭回归结果

    12864
    $ \lambda $2.002.00
    测试$ {R^2} $0.940.92
    MSE0.010.02
    下载: 导出CSV

    表  6  ${{{P}}_{{\text{tr}}}}$上表征向量为128维,多元2阶多项式系数绝对值超过0.5的项

    v_95×v_96v_119v_8v_70v_63v_55v_115v_12v_89v_85v_29v_77v_37v_65v_84v_43v_111v_20
    系数0.500.560.560.640.650.660.690.740.750.770.850.870.920.921.261.281.291.47
    下载: 导出CSV

    表  7  ${{{P}}_{{\text{tr}}}}$上表征向量为64维,多元2阶多项式系数绝对值超过0.4的项

    v_21v_45v_56v_31×v_32v_13v_28v_27v_33
    系数–0.54–0.410.400.410.420.470.490.78
    下载: 导出CSV

    表  8  两个表征向量维度下${{{P}}_{{\text{te}}}}$上多项式岭回归结果

    12864
    $ \lambda $2.002.00
    测试R20.950.92
    MSE0.010.02
    下载: 导出CSV

    表  9  ${{{P}}_{{\text{te}}}}$上表征向量为128维,多元2阶多项式系数绝对值超过0.5的项

    v_70v_63v_119v_89v_55v_115v_29v_12v_77v_85v_65v_37v_43v_84v_111v_20
    系数0.540.560.600.640.640.720.760.840.840.850.890.941.141.151.171.31
    下载: 导出CSV

    表  10  ${{{P}}_{{\text{te}}}}$上表征向量为64维,多元2阶多项式系数绝对值超过0.4的项

    v_21v_31v18×v19v_13v_28v_27v_33
    系数–0.440.400.420.420.460.520.78
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-27
  • 修回日期:  2022-05-22
  • 录用日期:  2022-06-01
  • 网络出版日期:  2022-06-07
  • 刊出日期:  2023-02-07

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