## 8 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **IDS Architecture** & **Techniques Used** & **Methodology** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{[PERSON] et al. [48]} & \multirow{4}{*}{Network-based} & \multirow{4}{*}{AE} & proposed Model to perform feature & \(\bullet\) & Lightweight. & \(\bullet\) & Low detection accuracy. \\  & & & reconstruction and & & recover & \(\bullet\) & High \\  & & & detect malicious in & & categorical & \(\bullet\) & High \\  & & & IoT environment. & & features. & \\ \hline \multirow{4}{*}{[PERSON] et al. [15]} & \multirow{4}{*}{Network-based} & \multirow{4}{*}{VAE + DNN} & \multirow{4}{*}{VAE + DNN} & proposed model to & \(\bullet\) & Lightweight. & \(\bullet\) \\  & & & unknown attacks using AE and DNN to learn the complex traffics and imbalanced classes. & & Low resource & \(\bullet\) & High \\ \hline \multirow{4}{*}{[PERSON] et al. [30]} & \multirow{4}{*}{Network-based} & \multirow{4}{*}{LSTM} & proposed model that & \(\bullet\) & High detection accuracy. & \(\bullet\) & Multi-classification method needs to be enhanced. \\  & & & & concept of the drift & \(\bullet\) & work well for \\  & & & method to improve the accuracy of anomaly & \(\bullet\) & Memory effective. & \(\bullet\) & the enhanced. \\  & & & detection using LSTM. & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [14]} & \multirow{4}{*}{Network-based} & \multirow{4}{*}{DBN} & Proposed an intelligent IDS to detect malicious traffic in IoT networks using DBN. & \(\bullet\) & Real-Time IDS. & \(\bullet\) & Detection accuracy needs to be enhanced. \\ \hline \multirow{4}{*}{[PERSON] et al. [16]} & \multirow{4}{*}{Network-based} & \multirow{4}{*}{LSTM + RNN} & Proposed approach is to analyze a series of \(\bullet\) network packets to detect botnets using & \(\bullet\) & Enhanced & \(\bullet\) & Few types of attacks. \\  & & & LSTM + RNN for & \(\bullet\) & Robustness. & \(\bullet\) & High \\  & & & detect botnets using & \(\bullet\) & High detection accuracy. & \(\bullet\) \\  & & & & lightweight. & \(\bullet\) & Resources \\ \hline \multirow{4}{*}{[PERSON] et al. [17]} & \multirow{4}{*}{Network-based} & \multirow{4}{*}{CNN} & \multirow{4}{*}{CNN} & \multirow{4}{*}{High detection accuracy.} & \(\bullet\) & High detection accuracy. & \(\bullet\) & High \\  & & & & Detect point & & computational \\  & & & DeepAnTI model to & & anomalies, & time. \\  & & & & contextual & \(\bullet\) & Poor data quality \\  & & & & time series prediction. & \(\bullet\) & Model works well & data \\  & & & & with a vast & & modeling phase. \\ \hline \multirow{4}{*}{[PERSON] et al. [41]} & \multirow{4}{*}{Network-based} & \multirow{4}{*}{AE} & Proposed N-BalIoT to extract network traffics and detect anomalies from resource & \(\bullet\) & Enhanced & \(\bullet\) & Low traffic prediction. \\  & & & & robustness & \(\bullet\) & Detection accuracy \\  & & & & constraint devices. & \(\bullet\) & detect attacks. & not reported. \\ \hline \multirow{4}{*}{[PERSON] et al. [40]} & \multirow{4}{*}{Centralized Host-Based} & \multirow{4}{*}{DBN} & Proposed approach to detect anomaly attacks in IoT environment. & \(\bullet\) & High detection accuracy. & \(\bullet\) & Not a Real-Time IDS. & \(\bullet\) \\  & & & & lightweight. & \(\bullet\) & Detect few types of IoT attacks. \\ \hline \multirow{4}{*}{[PERSON] et al. [46]} & \multirow{4}{*}{Network-based} & \multirow{4}{*}{CNN + VAE + LSTM} & Proposed approach to detect anomaly in IoT networks by combining three deep learning techniques. & \(\bullet\) & High detection accuracy. & \(\bullet\) & Resource-consuming. \\  & & & & networks by combining & \(\bullet\) & High \\  & & & & learning techniques. & \(\bullet\) & Lightweight. & \(\bullet\) \\ \hline \multirow{4}{*}{[PERSON] et al. [34]} & \multirow{4}{*}{Network-based} & \multirow{4}{*}{CNN} & Proposed approach to detect anomaly in IoT environment with focusing on DoS attacks. & \(\bullet\) & High detection accuracy. & \(\bullet\) & Detect few types of IoT attacks. \\  & & & & detection accuracy. & \(\bullet\) & High \\  & & & & focusing on DoS attacks. & \(\bullet\) & Lightweight. & \(\bullet\) \\ \hline \multirow{4}{*}{[PERSON] et al. [35]} & \multirow{4}{*}{Host-based} & \multirow{4}{*}{CNN} & Proposed approach to monitoring malicious botnet on resource constraint IoT devices using three types of IoT devices. & \(\bullet\) & \(\bullet\) & Expensive power monitor. \\  & & & & monitoring malicious & \(\bullet\) & Good & \(\bullet\) & Detection accuracy needs to be enhanced. \\ \hline \multirow{4}{*}{[PERSON] et al. [42]} & \multirow{4}{*}{Host-based} & \multirow{4}{*}{AE} & Proposed approach to detect malicious in web server during users’ requests processing considering the MyBB web server as a case study. & \(\bullet\) & High False \\  & & & & \(\bullet\) & High value rate. \\ \cline{1-1}  & & & & \(\bullet\) & High computation \\ \cline{1-1}  & & & & \(\bullet\) & computational \\ \cline{1-1}  & & & & \(\bullet\) & No comparison with previous methods. \\ \hline \hline \end{tabular}
\end{table}