## 3 Result
### Datasets
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**S.No**} & \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**ML Techniques**} & \multirow{2}{*}{**IoT Attacks**} & \multicolumn{2}{c}{**Monitoring**} & \multirow{2}{*}{**Used Datasets**} \\  & & & & **Metrics** & & **DevSecOps** \\ \hline \multirow{4}{*}{S2} & \multirow{4}{*}{([PERSON] et al., 2019) [65]} & \multirow{4}{*}{EL} & \begin{tabular}{c} Sinkhole, local \\ repair attacks, \\ blackhole, sybil, \\ DODS, hello \\ flooding and \\ selective \\ forwarding. \\ \end{tabular} & \multirow{4}{*}{\begin{tabular}{c} Accuracy and \\ AUC \\ \end{tabular} } & \multirow{4}{*}{No.} & \multirow{4}{*}{RPL-NIDDS17} \\  & & & \begin{tabular}{c} Sinkhole, local \\ repair attacks, \\ blackhole, sybil, \\ DODS, hello \\ flooding and \\ selective \\ forwarding. \\ \end{tabular} & \multirow{4}{*}{\begin{tabular}{c} DOS \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Accuracy and \\ AUC \\ \end{tabular} } & \multirow{4}{*}{No.} & \multirow{4}{*}{RPL-NIDDS17} \\  & & & & & \\ \hline \multirow{2}{*}{S14} & \multirow{2}{*}{([PERSON] et al., 2019) [66]} & \multirow{2}{*}{NN, Nand} & \multirow{2}{*}{\begin{tabular}{c} DOS \\ \end{tabular} } & \multirow{2}{*}{Accuracy} & \multirow{2}{*}{No.} & \multirow{2}{*}{CICIDS2017} \\  & & & & & \\ \hline \multirow{4}{*}{S15} & \multirow{2}{*}{([PERSON] et al., 2019) [67]} & \multirow{2}{*}{NN.} & \begin{tabular}{c} Information \\ theft attacks, \\ DDOS, \\ reconnaissance \\ and DOS. \\ \end{tabular} & \begin{tabular}{c} Accuracy, \\ precision, recall, \\ and F-measure. \\ \end{tabular} & \multirow{2}{*}{No.} & \multirow{2}{*}{BoT-IoT} \\  & & & & & \\ \hline \multirow{4}{*}{S23} & \multirow{2}{*}{([PERSON] et al., 2018) [68]} & \multirow{2}{*}{Clustering.} & \begin{tabular}{c} Probing, DOS, \\ U2R and R2L. \\ Accuracy. \\ \end{tabular} & \begin{tabular}{c} Detection rate, \\ False Positive \\ rate and \\ Accuracy. \\ \end{tabular} & \multirow{2}{*}{No.} & \multirow{2}{*}{intelloT} \\  & & & & \\ \hline \multirow{4}{*}{S40} & \multirow{2}{*}{([PERSON] et al., 2020) [69]} & \multirow{2}{*}{NN.} & \begin{tabular}{c} Probing and \\ DOS. \\ \end{tabular} & \
\begin{tabular}{c} Accuracy, \\ precision, recall \\ and F1 score. \\ \end{tabular} & \multirow{2}{*}{No.} & \multirow{2}{*}{DS2 OS} \\  & & & & & \\ \hline \multirow{4}{*}{S43} & \multirow{2}{*}{([PERSON] et al., 2020) [70]} & \multirow{2}{*}{NN.} & \multirow{2}{*}{DDOS.} &
\begin{tabular}{c} Accuracy, \\ Fl-score, \\ precision, and \\ recall \\ \end{tabular} & \multirow{2}{*}{No.} & \multirow{2}{*}{CICIDS2017} \\  & & & & \\ \hline \multirow{2}{*}{S49} & \multirow{2}{*}{([PERSON] et al., 2020) [71]} & \multirow{2}{*}{NN.} & \multirow{2}{*}{DOS.} & \multirow{2}{*}{F1 score} & \multirow{2}{*}{No.} & \multirow{2}{*}{MedBIoT.} \\  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Other public datasets. In our study, we observed that the NSL-KDD dataset was used in 15 primary studies. The NSL-KDD dataset was created using three different protocols (TCP, UDP and ICMP). Two test datasets were developed by NSL-KDD--namely, KDDTest+ and KDDTest-21, which have 41 features [76]. This dataset is grouped into different attack categories--namely, R2L, Probe, U2R, and DoS. The UNSW-NB15 datasets, used in six primary studies, were generated by the Australian Centre's Cyber Range Lab [74]. This dataset varies from previous datasets such as NSL-KDD, which has fewer networks, more repetition, and fewer features. The UNSW-NB15 datasets include 49 features and nine attacks. These attacks include backdoors, fuzzers, analysis, exploits, generic, reconnaissance, shellcode, worms, and DoS. KDDCUP99, used in five primary studies, was generated by DARPA [73]. This dataset contains around 5 million samples of network captured packets. The KDDCUP99 datasets have 41 features and three attacks. These attacks include DoS, Probe, R2L, and U2R. The KDDCUP99 datasets contain many redundant and duplicated records.