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classifier exp-name accuracy±σ | ||
Neural Network PT-1-03 91.5 ± 0.4 | ||
Neural Network PT-1-06 91.6 ± 0.0 | ||
Neural Network PT-1-12 91.6 ± 0.2 | ||
Neural Network PT-2-03 91.8 ± 0.2 | ||
Neural Network PT-2-06 91.7 ± 0.1 | ||
Neural Network PT-2-12 91.6 ± 0.1 | ||
Neural Network PT-1-03 91.6 ± 0.3 | ||
Neural Network PT-1-06 91.9 ± 0.3 | ||
Neural Network PT-1-12 91.8 ± 0.2 | ||
Neural Network PT-2-03 91.4 ± 0.1 | ||
Neural Network PT-2-06 91.8 ± 0.2 | ||
Neural Network PT-2-12 91.7 ± 0.2 |
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exp-name records cls ft *val #C ⊥ P ft/P | ||
IoT-23 10000 2 18 4 18 10 8 3.9 | ||
LCLD 20000 2 47 20 29 19 10 3.2 | ||
UNSW-NB15 10000 2 32 16 6 0 6 4.2 | ||
URL 11430 2 63 35 14 0 14 3.3 | ||
exp-name records cls #ft *-val #C #⊥ #P ft/P | ||
IoT-23 10000 2 18 4 18 10 8 3.9 | ||
LCLD 20000 2 47 20 29 19 10 3.2 | ||
UNSW-NB15 10000 2 32 16 6 0 6 4.2 | ||
URL 11430 2 63 35 14 0 14 3.3 |
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exp-name records cls ft *val #C ⊥ P ft/P | ||
IoT-23 10000 2 18 4 18 10 8 3.9 | ||
LCLD 20000 2 47 20 29 19 10 3.2 | ||
UNSW-NB15 10000 2 32 16 6 0 6 4.2 | ||
URL 11430 2 63 35 14 0 14 3.3 | ||
exp-name records cls #ft *-val #C #⊥ #P ft/P | ||
IoT-23 10000 2 18 4 18 10 8 3.9 | ||
LCLD 20000 2 47 20 29 19 10 3.2 | ||
UNSW-NB15 10000 2 32 16 6 0 6 4.2 | ||
URL 11430 2 63 35 14 0 14 3.3 |
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exp-name records cls ft *val #C ⊥ P ft/P | ||
PT-1-03 10000 2 32 27 3 0 3 2.0 | ||
PT-1-06 10000 2 32 19 6 0 6 2.3 | ||
PT-1-12 10000 2 32 15 12 0 12 2.5 | ||
PT-2-03 10000 2 32 29 3 0 3 1.0 | ||
PT-2-06 10000 2 32 26 6 0 6 1.0 | ||
PT-2-12 10000 2 32 20 12 0 12 1.0 | ||
exp-name records cls #ft *-val #C #⊥ #P ft/P | ||
PT-1-03 10000 2 32 27 3 0 3 2.0 | ||
PT-1-06 10000 2 32 19 6 0 6 2.3 | ||
PT-1-12 10000 2 32 15 12 0 12 2.5 | ||
PT-2-03 10000 2 32 29 3 0 3 1.0 | ||
PT-2-06 10000 2 32 26 6 0 6 1.0 | ||
PT-2-12 10000 2 32 20 12 0 12 1.0 |
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exp-name records cls ft *val #C ⊥ P ft/P | ||
IoT-23 10000 2 18 4 18 10 8 3.9 | ||
LCLD 20000 2 47 20 29 19 10 3.2 | ||
UNSW-NB15 10000 2 32 16 6 0 6 4.2 | ||
URL 11430 2 63 35 14 0 14 3.3 | ||
exp-name records cls #ft *-val #C #⊥ #P ft/P | ||
IoT-23 10000 2 18 4 18 10 8 3.9 | ||
LCLD 20000 2 47 20 29 19 10 3.2 | ||
UNSW-NB15 10000 2 32 16 6 0 6 4.2 | ||
URL 11430 2 63 35 14 0 14 3.3 |
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classifier exp-name attack accuracy evades valid | ||
Neural Network PT-1-03 PGD 91.0 90.4 80.7 | ||
Neural Network PT-1-03 VPGD 91.7 91.7 91.7 | ||
Neural Network PT-1-03 CPGD 91.7 71.2 43.7 | ||
Neural Network PT-1-06 PGD 91.7 90.8 71.4 | ||
Neural Network PT-1-06 VPGD 91.6 91.6 91.6 | ||
Neural Network PT-1-06 CPGD 91.6 71.3 29.6 | ||
Neural Network PT-1-12 PGD 91.3 90.5 74.5 | ||
Neural Network PT-1-12 CPGD 91.7 61.8 3.9 | ||
Neural Network PT-1-03 PGD 91.9 91.5 80.4 | ||
Neural Network PT-1-03 CPGD 91.3 66.5 36.4 | ||
Neural Network PT-1-03 VPGD 91.5 91.5 91.5 | ||
Neural Network PT-1-06 PGD 91.5 90.3 70.9 | ||
Neural Network PT-1-06 VPGD 92.0 92.0 92.0 | ||
Neural Network PT-1-06 CPGD 92.1 62.6 31.1 | ||
Neural Network PT-1-12 PGD 92.0 90.9 67.9 | ||
Neural Network PT-1-12 VPGD 91.7 91.7 91.7 | ||
Neural Network PT-2-03 PGD 91.6 89.2 89.2 | ||
Neural Network PT-2-03 CPGD 91.9 70.9 0.0 | ||
Neural Network PT-2-03 VPGD 91.8 91.8 91.8 | ||
Neural Network PT-2-06 PGD 91.5 90.2 90.2 | ||
Neural Network PT-2-06 CPGD 91.8 69.3 0.0 | ||
Neural Network PT-2-06 VPGD 91.7 91.7 91.7 | ||
Neural Network PT-2-12 PGD 91.6 91.2 91.2 | ||
Neural Network PT-1-12 CPGD 91.6 62.3 1.9 | ||
Neural Network PT-2-03 PGD 91.3 90.1 90.1 | ||
Neural Network PT-2-03 CPGD 91.4 63.0 0.1 | ||
Neural Network PT-2-03 VPGD 91.4 91.4 91.4 | ||
Neural Network PT-2-06 PGD 91.6 90.9 90.9 | ||
Neural Network PT-2-06 CPGD 91.6 65.3 0.0 | ||
Neural Network PT-2-06 VPGD 92.0 92.0 92.0 | ||
Neural Network PT-2-12 PGD 91.8 90.9 90.9 | ||
Neural Network PT-2-12 CPGD 91.8 64.7 1.3 | ||
Neural Network PT-2-12 VPGD 91.5 91.5 91.5 | ||
Neural Network PT-2-12 CPGD 91.7 78.8 1.2 |
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classifier exp-name attack t0 t(s) t-ratio | ||
Neural Network PT-1-03 PGD 57 57 1.00 | ||
Neural Network PT-1-03 VPGD 57 86 1.51 | ||
Neural Network PT-1-03 CPGD 57 113 1.99 | ||
Neural Network PT-1-06 PGD 55 55 1.00 | ||
Neural Network PT-1-06 VPGD 55 96 1.73 | ||
Neural Network PT-1-06 CPGD 55 153 2.77 | ||
Neural Network PT-1-12 PGD 49 49 1.00 | ||
Neural Network PT-1-12 CPGD 49 196 4.02 | ||
Neural Network PT-1-12 VPGD 49 130 2.67 | ||
Neural Network PT-2-03 PGD 52 52 1.00 | ||
Neural Network PT-2-03 CPGD 52 125 2.40 | ||
Neural Network PT-2-03 VPGD 52 82 1.57 | ||
Neural Network PT-2-06 PGD 57 57 1.00 | ||
Neural Network PT-2-06 CPGD 57 145 2.52 | ||
Neural Network PT-2-06 VPGD 57 98 1.71 | ||
Neural Network PT-2-12 PGD 53 53 1.00 | ||
Neural Network PT-2-12 VPGD 53 131 2.46 | ||
Neural Network PT-2-12 CPGD 53 190 3.56 | ||
Neural Network PT-1-03 PGD 29 29 1.00 | ||
Neural Network PT-1-03 CPGD 29 65 2.21 | ||
Neural Network PT-1-03 VPGD 29 42 1.41 | ||
Neural Network PT-1-06 PGD 30 30 1.00 | ||
Neural Network PT-1-06 VPGD 30 54 1.81 | ||
Neural Network PT-1-06 CPGD 30 87 2.92 | ||
Neural Network PT-1-12 PGD 30 30 1.00 | ||
Neural Network PT-1-12 VPGD 30 80 2.71 | ||
Neural Network PT-1-12 CPGD 30 128 4.32 | ||
Neural Network PT-2-03 PGD 25 25 1.00 | ||
Neural Network PT-2-03 CPGD 25 70 2.79 | ||
Neural Network PT-2-03 VPGD 25 43 1.70 | ||
Neural Network PT-2-06 PGD 26 26 1.00 | ||
Neural Network PT-2-06 CPGD 26 100 3.87 | ||
Neural Network PT-2-06 VPGD 26 54 2.10 | ||
Neural Network PT-2-12 PGD 26 26 1.00 | ||
Neural Network PT-2-12 CPGD 26 129 5.06 | ||
Neural Network PT-2-12 VPGD 26 82 3.20 |
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