## 7 Outcomes
### RQ2: What Is the Performance of Anomaly Intrusion Detection in IoT Using Deep Learning Techniques?  
Accuracy, precision, recall, false-positive rate (FPR), false-negative rate (FNR), and f-measure are the most frequently employed model evaluation techniques based on deep learning [50; 51; 52; 53; 54].  
As shown in Table 3, the high accuracy is nearly 100%; precision and recall are almost 100% in D-PACK [45]. They used CNN and AE techniques on the Mirai-RGU dataset. However, this model takes a long time for training and preprocessing, which is resource consuming. Additionally, it covers only a few types of attacks. Similarly, the study conducted by [37] used CNN and AE techniques on the Yahoo Webscope S5 dataset and achieved 99.62% accuracy, 98.78% precision, and 97.2% recall. This indicates that the combination of CNN and AE may improve the performance. Nevertheless, the resource-consuming aspect, network overhead, and datasets with real IoT traffic should be considered as well. [PERSON] et al. [47] proposed a model that achieved an accuracy of 99.78%, precision of 98.99%, recall of 91.05%, and FAR of 0.22%, using DML techniques by using  
Figure 5: Taxonomy of anomaly intrusion detection in IoT using deep learning techniques [13; 14; 15; 16; 17; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49].  
the KDDUP99 dataset. However, this model suffers from high resource consumption, and the dataset does not contain IoT traffics and modern types of attacks. [PERSON] and [PERSON] (2016) proposed a model that achieved 99.36% accuracy, the precision of 97.97%, and recall of 98.86%, using LSTM with RNN techniques. However, they did not report the FAR, as the model is for a specific type of attack and is resource consuming. We can say that combining AE with CNN techniques could enhance the accuracy and decrease the FAR, but we should consider the resource consumption and cover the IoT attacks. Figure 6 shows the frequency of the performance measures of the studies.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Study** & **Techniques** & **Accuracy** & **Precision** & **Recall** & **FAR** & **F1-Measure** & **FPR** & **FNR** \\ \hline [PERSON] et al. (2018) & AE & 80\% & 81.59\% & 80.1\% & & 79.08\% & \\ [PERSON] et al. (2015) & VAE + DNN & 89.08\% & 86.05 & 95.68 & & 90.61 & 19.01 & \\ [PERSON] et al. (2010) & LSTM & 98\% & & & & & \\ [PERSON] et al. (2014) & DBN & 97\% & & & & & \\ [PERSON] et al. (2016) & LSTM + RNN & 99.36\% & 97.97\% & 98.86\%, & & 98.42 & & \\ [PERSON] et al. (2017) & CNN & 99\% & 100\% & & & & \\ [PERSON] et al. (2014) & AE & & & & & & \\ [PERSON] et al. (2014) & DBN & 98.37\% & 97.21\%, & 98.34\% & & 97\% & \\ [PERSON] et al. (2016) & CCN + VAE + LSTM & 99\% & & & & & \\ [PERSON] et al. (2014) & CNN & 99\% & & & & & \\ [PERSON] et al. (2015) & CNN & 96.50\%, & & & 85\% & & \\ [PERSON] et al. (2014) & AE & & & & & & \\ [PERSON] et al. (2013) & deep learning & & & & & & \\ [PERSON] et al. (2013) & CNN + LSTM & 94.30\% & 93.48\% & 93.67\% & & 93.58\% & 5.20\% \\ [PERSON] et al. (2014) & CNN & 99.88\% & 99.89\% & 97.94\% & & 98.64\% & \\ [PERSON] et al. (2018) & DFFNN & 98.4\%, & & 99\%, 93\% & & & 1.8\%, 8.2\% \\ [PERSON] et al. (2014) & LSTM & & & & & & \\ [PERSON] et al. (2016) & CNN & 86.95\% & & & & & \\ [PERSON] et al. (2012) & LSTM & 97.58\% & & 83.79\% & 2.02\% & & 6.02\% \\ [PERSON] et al. (2014) & AE & 99.81\% & & & & & 100\% & 0\% & \\ [PERSON] et al. (2015) & CNN + AE & 100\% & 100\% & 100\% & & 100\% & 0\% & \\ [PERSON] et al. (2017) & CNN + AE & 99.62\% & 98.78\% & 97.2\% & & 98.78\% & \\ [PERSON] et al. (2014) & AE & 99.6 & 100\% & 100\% & 100\% & 0.0057 & & \\ [PERSON] et al. (2018) & AE & 97.85\% & 100\% & 100\% & & 85.42\% & \\ [PERSON] et al. (2019) & GNN/RNN & 99\% & & & & & \\ [PERSON] et al. (2017) & DML & 99.78 & 98.99 & 91.05 & 0.22\% & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of the studies models.  
Figure 6: The frequency of performance measures of the studies.