# SEHIDS: Self Evolving Host-Based Intrusion Detection System for IoT Networks
## 5 Results and Discussion
### Assessment of SEHIDS with ReNN Architecture (Anomaly-Based IDS)
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Ref.** & **Architecture** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Training Time (s)** & **Testing Time (s)** \\ \hline \hline  & [80] & LR & 0.61 & 0.37 & 0.61 & 0.46 & 3.023 & 0.005 \\ \hline
[80] & LDA & 0.68 & 0.74 & 0.68 & 0.62 & 1.041 & 0.004 \\ \hline
[80] & kNN & 0.84 & 0.85 & 0.84 & 0.84 & 58.018 & 109.361 \\ \hline
[80] & RF & 0.85 & 0.87 & 0.85 & 0.85 & 10.884 & 0.164 \\ \hline
[80] & CART & 0.88 & 0.90 & 0.88 & 0.88 & 6.308 & 0.022 \\ \hline
[80] & NB & 0.62 & 0.63 & 0.62 & 0.51 & 0.21 & 0.069 \\ \hline
[80] & SVM & 0.61 & 0.37 & 0.61 & 0.456 & 3525.052 & 558.663 \\ \hline
[80] & LSTM & 0.81 & 0.83 & 0.81 & 0.80 & 1596.809 & 9.023 \\ \hline
[81] & AE + LSTM + iForest & - & 0.7059 & 0.9001 & 0.7913 & 396.317 & 2.142 \\ \hline
[81] & OC-SVM & - & 0.7004 & 0.4047 & 0.5130 & 4659.594 & 1431.322 \\ \hline
[81] & iForest & - & 0.7634 & 0.3650 & 0.4939 & 19.586 & 5.158 \\ \hline
[81] & LSTM-AE & - & 0.6565 & 0.3025 & 0.4142 & 1900.681 & 55.876 \\ \hline
[81] & ID-CNN-GRU & - & 0.4699 & 0.8442 & 0.6038 & 575.203 & 15.318 \\ \hline
[81] & AE & - & 0.625 & 0.3201 & 0.4233 & 97.403 & 2.465 \\ \hline
[81] & DAGMM & - & 0.6264 & 0.4810 & 0.5442 & 263.947 & 0.173 \\ \hline
[82] & Mini Batch K-Means & 0.8772 & 0.933 & 0.698 & 0.799 & - & - \\ \hline
[82] & OPTICS & 0.7935 & 0.633 & 0.967 & 0.765 & - & - \\ \hline
[82] & Fuzzy C-Means & 0.9105 & 0.993 & 0.748 & 0.854 & - & - \\ \hline
**This work** & **ReNN +** & **0.999** & **0.999** & **0.999** & **0.999** & **2.325** & **7.104** \\ \hline
[83] & LSTM & 0.9990 & 0.9995 & 0.9995 & 0.9995 & - & - \\ \hline
[83] & BiLSTM & 0.9996 & 0.9997 & 0.9997 & 0.9997 & - & - \\ \hline
[83] & GRU & 0.9993 & 0.9997 & 0.9997 & 0.9997 & - & - \\ \hline
[84] & RNN-BPTT & 0.9820 & - & & & - & - \\ \hline
[85] & MLP & 0.7901 & - & - & - & - & - \\ \hline
[85] & CNN & 0.9127 & - & - & 0.9780 & - & - \\ \hline
[86] & RNN & 0.9831 & 0.9750 & - & 0.9980 & - & - \\ \hline
[87] & ANN & 0.9723 & - & - & - & - & - \\ \hline
[87] & GRU & 0.9976 & - & - & - & - & - \\ \hline
[88] & DNN & 0.9980 & 0.9780 & - & - & - & - \\ \hline
[88] & CNN & 0.9910 & 0.9890 & - & - & - & - \\ \hline
[88] & RNN & 0.9990 & 0.9970 & - & - & - & - \\ \hline
[88] & MTL & 0.9990 & 0.9980 & - & - & - & - \\ \hline
[89] & DNN & 0.9990 & - & - & - & - & - \\ \hline
**This work** & **ReNN +** & **1.00** & **1.00** & **1.00** & **1.00** & **3.340** & 8.748 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between the performance metrics of SEHIDS based models and some related works. LR: Logistic Regression, LDA: Linear Discriminant Analysis, RF: Random Forest, CART- Classification and Regression Trees, NB- Naive Bayes, CART- Classification and Regression Trees, LSTM- Long Short-Term Memory, AE: autoencoder, iForest: Isolation Forest, OC-SVM: One Class-SVM, CNN: Convolutional neuronal network, GRU: Gated Recurrent Unit, DAGMM: Deep Autoencoder Gaussian Mixture Model, OPTICS: Ordering Points to Identify the Clustering Structure, BiLSTM: Bidirectional, MTL: Multi-Task Learning, IG: Information Gain Ensemble, GRE: Gain Ratio Ensemble, UMF: Union Mathematical set theory FS, LFN: Single Hidden Layer Feed-Forward Neural Network. The results presented in Table 2 demonstrate the outperformance of SEHIDS compared to the other models, which affirms the ability of SEHIDS to develop anomaly-based IDS with the same efficiency as signature-based IDS. The generic performance is mainly attributed to the fact that SEHIDS does not define its own measure space to trigger the evolving process but uses the outcomes of the built-in loss function to do so. This, in turn, allows SEHIDS to act as a concordant concept-drift scheme through which the model can increase its computational power timely and without compromising its intrinsic operations.