## 3 Key Findings
### RQ2: What Kinds of Learning Algorithms Are Being Deployed Towards the Security of IIoT?
#### 3.2.4 RQ2-2: Set of Available Datasets Used by Learning Models for IIoTsec  
In RQ2-1, we discussed how learning algorithms are deployed in various approaches against numerous attacks in the IIoT ecosystem. We also highlighted different datasets that were used to validate the models in Table 2. However, our critical observation of the datasets reveals that the majority of the datasets are not IIoT-centric, coupled with the fact that a significant gap is witnessed in some security development solutions toward smart environments, like IIoT, without dataset [49; 83]. And when the dataset is even available, training and validating the fidelity of IIoTsec using a non-IIoT-centric dataset is also a vulnerability [39]. Therefore, in this subsection, the aim is to present IIoT-centric datasets. Henceforth, the research community in this niche would find it appropriate for validation. The dataset is the major component for learning algorithm prediction. However, datasets are attributes-oriented, identical, and highly sensitive. Whether the datasets are generated via experimental testbed or synthetically produced, they are all subjected to various noises and, most times, imbalance [49]. Also, the datasets are non-universal; a particular dataset does not fit all disciplines nor all scenarios in a single discipline. Therefore, a technical approach, such as feature engineering, is needed to determine an appropriate dataset that is usable and useful to the concept at hand. In this work, we found out that many available datasets are IoT based, such as CIC IoT 2022, MQTT-IOT-IDS2020, UNSW \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Citation** & **Technique** & **Point-of-Deployment** & **Key Contributions** & **Limitation** \\ \hline \multirow{2}{*}{[81]} & LRC sharding technology & \multirow{2}{*}{Data storage} & It addresses data leakage and scalability in IIoT & No consideration of possible data replica \\ \hline \multirow{2}{*}{[17]} & BLS-based proof of replication and VDF & & \multirow{2}{*}{Device’s trust management} & Mutual trust of the device was performed. Data replicas in cloud servers was mitigated & Scalability was not prioritized \\ \hline \multirow{2}{*}{[13]} & Digital twin, Blockchain & \multirow{2}{*}{Multi-party collaboration} & Provides a heuristic digital twin data scheduling framework that guarantees data privacy, efficiency, and security in an IIoT & Centralization of the model caused possible malicious behavior within \\ \hline \multirow{2}{*}{[82]} & Blockchain, SDN, and Cloud computing & IIoT devices and information-safe storage & It maximizes efficiency and security in cloud computing without any challenge to response time and CPU utilization & The proposed model is yet to be reliant on IIoT \\ \hline \multirow{2}{*}{[16]} & A Survey & Critical infrastructure & It presented various adopted authentications toward IIoTsec & Only considered is the authentication of devices \\ \hline \multirow{2}{*}{[6]} & Blockchain and Fog computing & Critical Infrastructure—CPS & It advances CPSs in terms of QoS, data storage, computing, and security & Deployment’s incompatibility with IIoT’s heterogeneity \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Blockchain technique for IIoTsec. NB15, Bot-IoT, and WUSTL-IIOT-2021. The research community has therefore extensively evaluated the proposal and deployment using the IoT-based dataset in different dimensions, as shown in Table 2. The findings are heart soothing, but a significant research gap is that if IoT-based datasets are more appropriate for IoT-based scenarios, it looks damning when it is extended to IIoT-based problems. The unique part of IIoT, the industrial concept, attracts a huge feature that should not be underestimated for reliable performance accuracy, especially for a security-tailored remedy. Consequently, we filtered the IIoT-based datasets, and their concepts are highlighted as follows.