# IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses
## 7 Conclusions and Future Direction
In [105], the approach was reported to have achieved an accuracy of 70% and 60% FPR. The approach in [80] showed a significant variation in accuracy between the attack scenarios, with a margin between 40-50%. The authors in [110] evaluated their work against simulated IoT networks, demonstrating 99% accuracy, while in [111], the approach was able to recover categorical features with an accuracy of 99%. With the other two remaining approaches, in [83], an overall accuracy of detection increased from around 96% to over 99% with the increase in layers (i.e., nodes), and the approach [113] showed a 99.5% accuracy for simulated networks and 98.47% accuracy when implemented on an experimental IoT testbed. These results show that there are differences, as might be expected, that result in a range of accuracies reported. There are two issues that can be highlighted based on this review. First, the resource-constrained nature of IoT devices is one of the main limitations that make the adoption of IDSs in IoT environments challenging. Conventional IDSs cannot be implemented in the IoT environment because of their computational cost. Such approaches require a heavy number of resources (e.g., memory/storage for data-classification purposes) not typically available in such environments. To that end, lightweight IDSs that need fewer resources are required. The second issue concerns centralized data analysis employed by IDSs, which is not fundamental in IoT. In this sense, anomaly-based IDSs are more suitable to the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & **Classification Accuracy** & **Prediction Accuracy** & **Resource Consumption** & **False-Positive Rate** & **Real-Time Capability** & **Scalability** & **Flexibility** & **Robustness** \\ \hline
[83] & \(\check{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{ \mathsf{\mathsf{\mathsf{ \mathsf{ \mathsf{ \mathsf{  \mathsf{  }}}}}}}}}}}}}\) & & \(\check{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{ \mathsf{\mathsf{\mathsf{\mathsf{\mathsf{ \mathsf{ \mathsf{ \mathsf{    \mathsf{    }}}}}}}}}}}} \)\times} & \(\check{\mathsf{\mathsf{\mathsf{\mathsf{\mathsf{\mathsfdistributed nature of IoT because they actually can minimize communication overhead, reduce network traffic, and are a less invasive cure to detecting malicious intrusions. Implementing distributed anomaly-based IDSs can be investigated as a solution [12; 14; 15; 24; 60; 69; 71; 72; 74; 75; 76; 77; 78; 82; 99] to providing a less invasive IDS. Regarding detection techniques, such as the anomaly-based IDSs, the preparation and testing time needed to accomplish the normal behavior of networks is high. A huge dataset is needed for training such IDSs to accomplish the normal behavior of the IoT network. Designing an IDS that can handle these problems is challenging. DL techniques using anomaly-based IDSs are a promising avenue for future research. Some other characteristics that future IoT-IDSs may have included a real-time approach with safe routing processes. We conclude that more attention must be paid to building a strong IDS as an intelligent system based on strong DL algorithms. This would help to prevent the onslaught of attacks to the victim (IoT device) and to improve the accuracy of detection. In summary, this manuscript documents a review of anomaly-based IDS systems used in IoT, deep learning techniques, intrusion detection systems, the IoT security, and various IoT attacks, including the various common architectures found in the IoT ecosystem. Furthermore, this study creates a novel mapping between various architectures to match them in term of structure, functionality, and security. The deep learning approaches for IDSs included supervised, unsupervised, and hybrid models. Specifically, this paper analyzed several deep learning approaches to anomaly-based IDSs in the literature.