## 4 Experimental Evaluation
### Experiment Results
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{3}{*}{**Models**} & \multicolumn{4}{c}{**Filtered**} & \multicolumn{4}{c}{**Unfiltered**} \\ \cline{2-9}  & \multicolumn{3}{c}{**5-Way Acc.**} & \multicolumn{3}{c}{**20-Way Acc.**} & \multicolumn{3}{c}{**5-Way Acc.**} & \multicolumn{3}{c}{**20-Way Acc.**} \\ \cline{2-9}  & **5-Shot** & **10-Shot** & **5-Shot** & **10-Shot** & **5-Shot** & **10-Shot** & **5-Shot** & **10-Shot** \\ \hline Gist + kNN [54] & 69.07 \(\pm\) 2.81 & 75.18 \(\pm\) 2.53 & 55.51 \(\pm\) 1.60 & 61.82 \(\pm\) 1.58 & 50.17 \(\pm\) 1.69 & 57.90 \(\pm\) 1.76 & 33.75 \(\pm\) 0.82 & 41.47 \(\pm\) 0.98 \\ N-Gram + kNN [17] & 46.57 \(\pm\) 2.18 & 49.37 \(\pm\) 2.04 & 36.67 \(\pm\) 1.09 & 38.30 \(\pm\) 1.80 & 36.05 \(\pm\) 2.03 & 41.64 \(\pm\) 3.24 & 25.33 \(\pm\) 1.46 & 31.22 \(\pm\) 2.13 \\ PE + kNN [26] & - & - & - & - & 57.18 \(\pm\) 1.10 & 66.56 \(\pm\) 1.00 & 41.77 \(\pm\) 0.82 & 51.09 \(\pm\) 0.84 \\ pixel kNN & 63.60 \(\pm\) 0.94 & 67.39 \(\pm\) 0.96 & 42.49 \(\pm\) 0.58 & 45.22 \(\pm\) 0.42 & 45.11 \(\pm\) 0.64 & 47.81 \(\pm\) 0.67 & 16.87 \(\pm\) 0.31 & 19.39 \(\pm\) 0.30 \\ \hline InductionNet [14] & 68.08 \(\pm\) 0.38 & 69.17 \(\pm\) 0.19 & 42.09 \(\pm\) 0.09 & 44.24 \(\pm\) 0.18 & 45.09 \(\pm\) 0.36 & 52.33 \(\pm\) 0.35 & 28.13 \(\pm\) 0.16 & 28.88 \(\pm\) 0.15 \\ SNAIL [38] & 73.81 \(\pm\) 0.36 & 75.13 \(\pm\) 0.35 & - & - & 52.87 \(\pm\) 0.35 & 57.29 \(\pm\) 0.34 & - & - \\ Meta-SGD [39] & 75.54 \(\pm\) 0.31 & 77.84 \(\pm\) 0.31 & 44.85 \(\pm\) 0.18 & 64.89 \(\pm\) 0.18 & 60.47 \(\pm\) 0.31 & 63.37 \(\pm\) 0.31 & 37.06 \(\pm\) 0.14 & 38.31 \(\pm\) 0.16 \\ ProtoNet [9] & 79.57 \(\pm\) 0.22 & 86.23 \(\pm\) 0.20 & 63.31 \(\pm\) 0.12 & 65.00 \(\pm\) 0.11 & 63.51 \(\pm\) 0.16 & 66.73 \(\pm\) 0.15 & 43.22 \(\pm\) 0.08 & 47.58 \(\pm\) 0.07 \\ RelationNet [8] & 74.98 \(\pm\) 0.23 & 77.28 \(\pm\) 0.23 & 53.13 \(\pm\) 0.12 & 57.48 \(\pm\) 0.13 & 61.41 \(\pm\) 0.16 & 60.05 \(\pm\) 0.17 & 38.45 \(\pm\) 0.08 & 39.50 \(\pm\) 0.09 \\ HABPN [15] & 80.19 \(\pm\) 0.20 & 82.24 \(\pm\) 0.21 & 56.34 \(\pm\) 0.19 & 59.44 \(\pm\) 0.18 & 58.78 \(\pm\) 0.23 & 65.62 \(\pm\) 0.15 & 40.32 \(\pm\) 0.09 & 40.67 \(\pm\) 0.08 \\ \hline
**ConvProtoNet** & **83.34 \(\pm\) 0.13** & **86.63 \(\pm\) 0.13** & **68.56 \(\pm\) 0.08** & **71.38 \(\pm\) 0.08** & **68.07 \(\pm\) 0.23** & **71.22 \(\pm\) 0.15** & **48.61 \(\pm\) 0.07** & **53.53 \(\pm\) 0.08** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Test accuracy and 95% confidence interval for malware few-shot classification on LargePE dataset. Bold items indicate the highest accuracy among all models under the same experiment setting. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Base Datasets**} & \multicolumn{4}{c}{**Tested Dataset**} \\ \cline{2-5}  & \
\begin{tabular}{c} **LargePE** \\ **(Filtered)** \\ \end{tabular} &
\begin{tabular}{c} **LargePE** \\ **(Unfiltered)** \\ \end{tabular} & **Microsoft** & **VirusShare** & **Drebin** \\ \hline LargePE (filtered) & **83.34 \(\pm\) 0.13** & 63.19 \(\pm\) 0.23 & 77.19 \(\pm\) 0.15 & 70.02 \(\pm\) 0.24 & 57.42 \(\pm\) 0.24 \\ LargePE (unfiltered) & 83.85 \(\pm\) 0.19 & **68.07 \(\pm\) 0.23** & 78.29 \(\pm\) 0.15 & 71.87 \(\pm\) 0.24 & 52.34 \(\pm\) 0.21 \\ VirusShare & 81.34 \(\pm\) 0.21 & 63.47 \(\pm\) 0.23 & 77.77 \(\pm\) 0.16 & **75.59 \(\pm\) 0.22** & 60.01 \(\pm\) 0.21 \\ Drebin & 74.61 \(\pm\) 0.24 & 52.91 \(\pm\) 0.24 & 73.69 \(\pm\) 0.19 & 62.21 \(\pm\) 0.25 & **68.58 \(\pm\) 0.22** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Test accuracy for malware 5-shot 5-way classification of ConvProtoNet across different datasets and 95% confidence interval is attached after accuracy. Base dataset refers to the dataset that model trains on and tested dataset refers to the dataset that uses as meta-testing set and the native accuracy of each datatset is in bold.