## 8 Discussion

Table 8: List of the state-of-the-art studies and the advantages and disadvantages. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **IDS Architecture** & **Techniques Used** & **Methodology** & **Advantages** & **Disadvantages** \\ \hline \hline [PERSON] et al. [13] & Distributed Network-Based & Multi-Layer deep learning & Proposed a distributed approach to detect attacks in social IoT. & \(\bullet\) & High detection accuracy. & \(\bullet\) & Few types of attacks. \\ \hline [PERSON] et al. [33] & Distributed Network-Based & CNN + LSTM & Proposed a distributed cloud-based approach to detect and mitigate phishing and Botnet attacks on client devices. & \(\bullet\) & Lightweight. & \(\bullet\) & Detection accuracy needs to be enhanced. \\ \hline [PERSON] et al. [49] & Centralized Host-Based & CNN & Proposed a semi-supervised based model to detect anomalies in IoT communication. & \(\bullet\) & Lightweight & \(\bullet\) & High computational complexity. \\ \hline [PERSON] et al. [38] & Network-based & DFFNN & Proposed anomaly detection to learn and validate the information collected from TCP/IP packets. & \(\bullet\) & Lightweight & \(\bullet\) & Not a Real-Time IDS. \\ \hline [PERSON] et al. [31] & Network-based & LSTM & Proposed approach to monitor and detect malicious from the network traffic flow. & \(\bullet\) & Lightweight. & \(\bullet\) & Detection accuracy needs to be enhanced. \\ \hline [PERSON] et al. [66] & Network-based & CNN & Proposed approach to detect intrusion in industrial IoT. & \(\bullet\) & Enhanced robustness. & \(\bullet\) & High computational complexity. \\ \hline [PERSON] et al. [32] & Network-based & LSTM & Proposed approach to detect attack interval from historic data in industrial IoT. & \(\bullet\) & Enhanced robustness. & \(\bullet\) & High computational complexity. \\ \hline [PERSON] et al. [43] & Host-based & AE & Proposed approach to the analysis of attack threats and abnormal behavior that deviates from normal behavior in IoT devices. & \(\bullet\) & Enhanced robustness. & \(\bullet\) & High training time. \\ \hline [PERSON] et al. [45] & Network-based & CNN + AE & Proposed approach to anomaly approach to detect features and predicting traffic with just a few first packets from each flow in IoT networks. & \(\bullet\) & High computational complexity. \\ \hline [PERSON] et al. [37] & Network-based & CNN + AE & Proposed approach to detect the anomaly and to enhance classification in time series. & \(\bullet\) & High detection accuracy. & \(\bullet\) & High computational complexity. \\ \hline \hline \multirow{4}{*}{[PERSON] et al. [44]} & \multirow{4}{*}{Network-based} & \multirow{4}{*}{AE} & Proposed CSSAE (cost-sensitive stacked auto-encoder) to solve the class imbalance problem in IDS and detect low-frequency attacks in IoT environment. & \(\bullet\) & High detection accuracy. & \(\bullet\) & High training time. \\ \hline \hline \multirow{4}{*}{[PERSON] et al. [18]} & \multirow{4}{*}{Network-based} & \multirow{4}{*}{AE} & Proposed model to distinguish reduction for the data and detect malicious at the IoT environment. & \(\bullet\) & High detection accuracy. & \(\bullet\) & High false alarm rate. \\ \cline{1-1} \cline{3-4}  & & & Proposed CSSAE (cost-sensitive stacked auto-encoder) to solve the class imbalance problem in IDS and detect low-frequency attacks in IoT environment. & \(\bullet\) & High detection accuracy. & \(\bullet\) & High training time. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._Table 8 also shows that there are many types of IDS architectures that have been implemented. Network-based IDS is the most applied architecture, due to the availability of labeled network traffic datasets. In the IoT networks, the architecture of the IDS depends on the application domain and the host environment [2]. The host-based approach is recommended to protect the operating system of the IoT devices from malicious attacks, while the network-based is suitable for protecting the communicated devices from malicious traffic.