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Table 2 Experimental results on phd-data. In Tables 1 and 2, the best experimental results are shown in boldface

From: DeepMal: maliciousness-Preserving adversarial instruction learning against static malware detection

Detector Baseline Obfuscation-based Feature-level DeepMal
  Prec Rec F1 Prec Rec F1 ΔF1 Prec Rec F1 ΔF1 Prec Rec F1 ΔF1
LR 0.9941 0.9927 0.9934 0.9940 0.9710 0.9824 0.0110 0.9915 0.6811 0.8075 0.1859 0.9259 0.2083 0.3401 0.6533
SVM 0.9970 0.9710 0.9838 0.9970 0.9652 0.9808 0.0030 0.9943 0.5072 0.6717 0.3121 0.9958 0.6956 0.8191 0.1647
RF 0.9606 0.9550 0.9578 0.9595 0.9275 0.9432 0.0146 0.9354 0.5666 0.7057 0.2521 0.9467 0.6956 0.8020 0.1558
Lenet-5 0.9841 0.9884 0.9862 0.9819 0.7894 0.8752 0.1110 0.9009 0.1449 0.2496 0.7366 0.8450 0.0869 0.1576 0.8286
All-Cov 0.9849 0.9492 0.9667 0.9848 0.9470 0.9629 0.0038 0.9333 0.2028 0.3333 0.6334 0.9167 0.1594 0.2716 0.6951
NiN 0.9642 0.9768 0.9704 0.9626 0.9347 0.9485 0.0219 0.8883 0.2884 0.4354 0.5350 0.8275 0.1739 0.2874 0.6830
VGG16 0.9524 0.9421 0.9473 0.9211 0.8994 0.9103 0.0370 0.9244 0.2328 0.5786 0.3687 0.8312 0.1018 0.4665 0.4808
ResNet 0.8999 0.8732 0.8865 0.8534 0.8213 0.8373 0.0492 0.7984 0.2085 0.5035 0.3830 0.8243 0.0876 0.4559 0.4306
Standard DNN 0.8867 0.8813 0.8840 0.8673 0.8575 0.8623 0.0217 0.5000 0.6375 0.5604 0.3236 0.8889 0.1000 0.1798 0.7066