## 3 The Proposed Approach of SCDNN
### The SCDNN Algorithm
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Dataset** & **Model** & **Normal** & **DoS** & **Probe** & **U2R** & **R2L** & **Acc** & **Recall** & **ER** \\ \hline \multirow{4}{*}{Dataset 1} & SVM & **98.21** & 83 & 66.01 & 0.88 & 3.14 & 81.52 & 77.72 & 18.48 \\  & BP & 96.51 & 89.49 & 46.18 & 9.21 & 1.93 & 85.66 & 83.48 & 14.34 \\  & RF & 93.65 & 96.62 & 59.27 & 0 & 0 & 90.44 & 91.08 & 9.56 \\  & Bayes & 91.51 & 95.59 & 61.35 & 4.39 & 3.56 & 89.48 & **92.57** & 10.52 \\  & SCDNN & 97.21 & **96.87** & **80.32** & **11.4** & **6.88** & **91.97** & 91.68 & **8.03** \\ \hline \multirow{4}{*}{Dataset 2} & SVM & 96.22 & 97.1 & 65.84 & 0 & 0.05 & 91.39 & 90.52 & 8.61 \\  & BP & 91.44 & **97.42** & 62.69 & **7.02** & **5.41** & 90.93 & **92.88** & 9.07 \\  & RF & 98.23 & 96.48 & 38.26 & 0 & 0 & 90.95 & 89.51 & 9.05 \\  & Bayes & 95.92 & 95.98 & 62.55 & 4.82 & 4.38 & 90.69 & 91.07 & 9.31 \\  & SCDNN & **98.42** & 97.2 & **70.64** & 3.51 & 1.57 & **92.03** & 91.35 & **7.97** \\ \hline \multirow{4}{*}{Dataset 3} & SVM & 95.87 & 97.23 & 64.86 & 0 & 0.06 & 91.41 & 90.59 & 8.59 \\  & BP & 81.53 & 96.95 & 8.81 & 6.14 & 7.26 & 88.03 & 90.05 & 11.97 \\  & RF & **99.57** & 96.57 & 0 & 0 & 0 & 90.76 & 89.37 & 9.24 \\  & Bayes & 96.38 & 96.29 & 59.15 & **7.02** & **7.46** & 91.12 & 90.95 & 8.88 \\  & SCDNN & 97.61 & **97.23** & **65.96** & 4.39 & 6.59 & **92.1** & **92.23** & **7.9** \\ \hline \multirow{4}{*}{Dataset 4} & SVM & 95.54 & 70.18 & 57.37 & 0 & 1.63 & 70.73 & 53.26 & 29.27 \\  & BP & 96.35 & 71.17 & **65.55** & 0 & 0.58 & 72.16 & **57.79** & 27.84 \\  & RF & **99.63** & 63.11 & 7.23 & 0 & 0 & 64.57 & 40.45 & 35.43 \\  & Bayes & 93.9 & 72.18 & 41.02 & 0 & 0 & 68.73 & 52.78 & 31.27 \\  & SCDNN & 96.17 & **75.84** & 53.37 & **3** & **3.01** & **72.64** & 57.48 & **27.36** \\ \hline \multirow{4}{*}{Dataset 5} & SVM & 98.57 & 18.93 & 49.89 & 0 & 0.11 & 54.1 & 20.45 & 45.9 \\  & BP & 91.79 & 7.63 & **66.58** & 1.5 & **2.43** & 49.53 & 27.56 & 50.47 \\ \cline{1-1}  & RF & **99.69** & 62.64 & 48.99 & 0 & 0 & 68.93 & 46.43 & 31.07 \\ \cline{1-1}  & Bayes & 99.06 & 61.65 & 35.4 & 0 & 0 & 66.87 & 44.28 & 33.13 \\ \cline{1-1}  & SCDNN & 97.19 & **74.51** & 48.37 & **5** & 0.62 & **71.83** & **55.08** & **28.17** \\ \hline \multirow{4}{*}{Dataset 6} & SVM & 95.81 & 41.5 & 43.67 & 0 & 0 & 41.46 & 30.6 & 58.54 \\ \cline{1-1}  & BP & 74.72 & 4.61 & **88.67** & 0 & 1.53 & 33.59 & 30.6 & 66.41 \\ \cline{1-1}  & RF & **99.72** & 36.15 & 6.74 & 0 & 0 & 32.73 & 18.9 & 67.27 \\ \cline{1-1}  & Bayes & 82.16 & **48.25** & 28.52 & 0 & 0 & 38.37 & 30.08 & 61.63 \\ \cline{1-1}  & SCDNN & 84.2 & **50.02** & 52.66 & **1.5** & **0.98** & **44.55** & **37.85** & **55.45** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparing network intrusion detection results for the six datasets (%). features of the data than other models. All methods are effective for intrusion detection with this dataset, except for the RF method, which has low precision and 0% detection accuracy for U2R and R2L attack types. For Dataset 2, the SCDNN attains higher precision for normal, DoS and probe traffic, with rates of 98.42%, 97.20% and 70.64%, respectively, and lower precision for U2R and R2L traffic than the BPNN. In this experiment the BPNN results in higher recall than other methods for the sparse attack types, which illustrates that the BPNN, with simple layer, has obtained the expected accuracy in a specific dataset, though its performance is unstable. The RF algorithm performs well for the more common traffic, but obtained 0% accuracy in detecting cases of U2R and R2L attacks. It may be that the construction of the trees used by this method can effectively classify most data, but some sparse attack types belong to false classes. In Dataset 3, the SCDNN performs best on DoS and probe intrusions, with precision rates of 97.23% and 64.96%, respectively. This result indicates that the proposed method is robust and captures

Figure 5: (**a–f**) Prediction accuracy histogram of the five detection models.