# Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark
## 3 Proposed Framework
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Classifier** & **Dataset** & **Trainable Params** & **Epoch** & **Batch Size** & **Training Time** & **Model Size** & **Accuracy** \\ \hline Autoencoder + TCN & Bot-IoT & 87,483 & 20 & 256 & 70 min & 1 MB & 100\% \\  & CICIDS2017 & 140,315 & 10 & 512 & 24 min & 2 MB & 99.99\% \\  & NSL-KDD (*a) & 173,505 & 100 & 256 & 25 min & 2 MB & 75.6\% \\  & NSL-KDD (*b) & 173,505 & 100 & 256 & 15 min & 2 MB & 99\% \\  & UNSW\_NB15 (*a) & 339,626 & 100 & 32 & 53 min & 2 MB & 75.7\% \\  & UNSW\_NB15 (*b) & 348,858 & 50 & 256 & 208 min & 3 MB & 97.9\% \\  & N-Balot & 167,073 & 20 & 128 & 39 min & 2 MB & 90.7\% \\  & KDD CUP 99 & 169,473 & 10 & 32 & 22 min & 2 MB & 99.9\% \\ Autoencoder + LSTM & Bot-IoT & 3,104,180 & 5 & 512 & 35 min & 36 MB & 100\% \\  & CICIDS2017 & 3,204,681 & 5 & 512 & 40 min & 37 MB & 99.4\% \\  & NSL-KDD (*a) & 3,291,591 & 100 & 256 & 90 min & 38 MB & 79\% \\  & NSL-KDD (*b) & 3,291,591 & 20 & 32 & 68 min & 38 MB & 98.9\% \\  & UNSW\_NB15 (*a) & 3,454,951 & 50 & 32 & 193 min & 40 MB & 42.3\% \\  & UNSW\_NB15 (*b) & 3,511,558 & 15 & 512 & 216 min & 40 MB & 97.9\% \\  & N-Balot & 3,276,174 & 10 & 256 & 42 min & 38 MB & 80.5\% \\  & KDD CUP 99 & 3,282,603 & 10 & 128 & 30 min & 38 MB & 99.9\% \\ Autoencoder + BRNN & Bot-IoT & 2,329,268 & 5 & 512 & 121 min & 27 MB & 100\% \\  & CICIDS2017 & 2,517,833 & 5 & 512 & 181 min & 29 MB & 99.4\% \\  & NSL-KDD (*a) & 2,686,663 & 50 & 256 & 350 min & 31 MB & 73.6\% \\  & NSL-KDD (*b) & 2,686,663 & 20 & 256 & 121 min & 31 MB & 51.9\% \\  & UNSW\_NB15 (*a) & 3,044,095 & 50 & 256 & 375 min & 35 MB & 31.9\% \\  & UNSW\_NB15 (*b) & 3,074,566 & 5 & 512 & 272 min & 35 MB & 95.7\% \\  & N-Balot\(\bar{T}\) & 2,656,910 & 5 & 256 & 181 min & 31 MB & 23.3\% \\  & KDD CUP 99 & 2,669,483 & 10 & 128 & 228 min & 31 MB & 79.2\% \\ Autoencoder + BLSTM & Bot-IoT & 8,825,780 & 5 & 512 & 56 min & 101 MB & 100\% \\  & CICIDS2017 & 9,014,345 & 5 & 512 & 88 min & 103 MB & 99.9\% \\  & NSL-KDD (*a) & 9,183,175 & 100 & 256 & 300 min & 105 MB & 76\% \\  & NSL-KDD (*b) & 9,183,175 & 20 & 32 & 145 min & 105 MB & 98.7\% \\  & UNSW\_NB15 (*a) & 9,540,607 & 50 & 256 & 156 min & 109 MB & 38.1\% \\  & UNSW\_NB15 (*b) & 9,571,078 & 5 & 512 & 300 min & 110 MB & 97.7\% \\  & N-Balot\(\bar{T}\) & 9,153,422 & 10 & 256 & 162 min & 105 MB & 90.8\% \\  & KDD CUP 99 & 9,165,995 & 10 & 128 & 105 min & 105 MB & 99.5\% \\ CNN + LSTM & Bot-IoT & 3,115,925 & 20 & 256 & 126 min & 36 MB & 100\% \\  & CICIDS2017 & 3,204,159 & 20 & 256 & 190 min & 37 MB & 100\% \\  & NSL-KDD (*a) & 3,285,909 & 20 & 256 & 18 min & 38 MB & 73.4\% \\  & NSL-KDD (*b) & 3,285,909 & 20 & 256 & 20 min & 38 MB & 98.9\% \\  & UNSW\_NB15 (*a) & 3,439,594 & 20 & 256 & 25 min & 39 MB & 52.7\% \\  & UNSW\_NB15 (*c) & 3,453,930 & 20 & 256 & 550 min & 39 MB & 97.7\% \\  & N-Balot\(\bar{T}\) & 3,271,675 & 20 & 256 & 125 min & 38 MB & 90.9\% \\  & KDD CUP 99 & 3,277,717 & 20 & 256 & 62 min & 38 MB & 99.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hybrid DL classifiers summary. nodes during the training process. The same layer structure was repeated in the TCN block and is shown in Figure 3.