## 2 Research Methodology
### Data Extraction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**S.No**} & \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**ML Techniques**} & \multirow{2}{*}{**IoT Attacks**} & \multirow{2}{*}{**Evaluation Metrics**} & \begin{tabular}{c} **Monitoring Real-Time** \\ **Security Using** \\ **DevSecOps** \\ \end{tabular} \\ \hline S1 & ([PERSON] et al., 2018) [23] & BN. & Probing, and DOS & \begin{tabular}{c} Precision, recall, \\ and F-measure. \\ \end{tabular} & No. \\ \hline S3 & ([PERSON] et al., 2019) [24] & BN and DT. & \begin{tabular}{c} DOS, MITM, \\ reconnaissance, \\ and replay. \\ \end{tabular} & \begin{tabular}{c} Precision, recall, \\ and F-measure. \\ \end{tabular} & No. \\ \hline S6 & ([PERSON] et al., 2017) [25] & DT and Clustering. & Wormhole & Detection rate. & No. \\ \hline S7 & ([PERSON] et al., 2019) [26] & DT & DOS & Accuracy. & No. \\ \hline S8 & ([PERSON] et al., 2019) [27] & SVM & \begin{tabular}{c} Selective \\ forwarding attack. \\ \end{tabular} & Accuracy. & No. \\ \hline S10 & ([PERSON] et al., 2019) [28] & DT, SVM and NN. & \begin{tabular}{c} Backdoor, \\ command \\ injection, and SQL \\ injection. \\ \end{tabular} & \begin{tabular}{c} Accuracy, false \\ alarm rate, ROC \\ curve, and \\ sensitivity matric. \\ \end{tabular} & No. \\ \hline S17 & ([PERSON] et al., 2018) [29] & NN. & DOS. & \
\begin{tabular}{c} Accuracy, true \\ positive rate, and \\ false positive rate. \\ \end{tabular} & No. \\ \hline S18 & ([PERSON] et al., 2019) & SVM. &
\begin{tabular}{c} Selective \\ forwarding, and \\ blackhole. \\ \end{tabular} & Accuracy. & No. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Internet of Things (IoT) device testbed datasets. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Characteristic**} & \multicolumn{1}{c}{**Research Question**} \\ \hline Authors, study title, publication year, publication title, source, source type & General \\ \hline IoT attack datasets & RQ1 \\ \hline IoT attack detection machine learning techniques & RQ2 \\ \hline Type of IoT attacks & RQ3 \\ \hline Dependent/independent variables & RQ4 \\ \hline Performance measures & RQ5 \\ \hline Monitoring real-time security for IoT systems using DevSecOps pipelines & RQ6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Data extraction characteristics related to the research questions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**S.No** & **Reference** & **ML Techniques** & **IoT Attacks** & **Evaluation Metrics** & **Monitoring Real-Time Security Using DevSecOps** \\ \hline S20 & ([PERSON] et al., 2017) [31] & SVM. & DOS. & Precision and recall. & No. \\ \hline S21 & ([PERSON] et al., 2016) [32] & SVM. & DOS and DDoS. & Precision and recall. & No. \\ \hline S22 & ([PERSON] et al., 2019) [33] & NN. & DOS & Detection rate. & Yes. \\ \hline S24 & ([PERSON] et al., 2019) [34] & NN. & DOS & Precision, recall, and F-score. & No. \\ \hline S26 & ([PERSON] et al., 2019) [35] & BN, DT and NN. & Amplification & Recall, accuracy, precision, and false positive rate. & No. \\ \hline S30 & ([PERSON] et al., 2018) [36] & NN. & Wormhole & Detection rate. & No. \\ \hline S31 & ([PERSON] et al., 2019) [37] & NN. & Wormhole & Detection rate. & No. \\ \hline S32 & ([PERSON], 2019) [38] & DT, Clustering and NN. & Wormhole & Accuracy, precision, and recall. & No. \\ \hline S33 & ([PERSON] et al., 2019) [39] & NN. & Wormhole & Precision, recall, and F1 score. & No. \\ \hline S38 & ([PERSON] et al., 2020) & SVM. & Blackhole and selective forwarding. & Accuracy, precision, negative predictive value (NPV), recall, and the Matthews correlation (MCC). & No. \\ \hline S41 & ([PERSON] et al., 2020) & SVM, clustering and DT. & DOS, scanning and MITM. & Accuracy, recall, and F1 score & No. \\ \hline S47 & ([PERSON] et al., 2020) & BN. & DOS, scanning and MITM & Accuracy, precision, recall, and F-measure & No. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._