## 3 Key Findings
### RQ2: What Kinds of Learning Algorithms Are Being Deployed Towards the Security of IIoT?
#### 3.2.3 Blockchain Technique  
The research community has considered distinct properties of emerging technologies to ameliorate security challenges in Industry 4.0. In this case, the blockchain technique also passes the integrity test, based on its decentralization and tamper-proof features, to be deployed against the assaults in IIoT [81]. In addition, [PERSON] et al. [6] carried out a study on the security challenges across CPSs with a focus on the smart industry. The authors considered security challenges in RQ1 and, therefore, identified six attributes of blockchain, such as cryptographic-based security design, immutable data structure; distributable system (SPOF-free), aggregated transactions ordering (blocks), P2P interaction, fault-tolerance, and gossip-based communication protocol, making it a better-deployed technique for IIoTsec. Likewise, refs. [6,81] considered scalability and data security leakage as an inference of the MFC loophole, which could be mitigated by the blockchain technique. Thus, the authors designed a highly scalable data storage mechanism and cryptographic accumulator-based data storage scheme to improve fault tolerance and block data storage leakage respectively using local repairable code (LRC) sharding technology combined with a bilinear accumulator. The proposed performance scheme proved better toward IIoTsec when compared with existing polynomial coding sharding in the literature.  
In the same vein, ref. [17] discovered that data insecurity is caused by the distrust between the devices of IIoT. The authors affirmed other research works toward the identified security loopholes but argued that they are unsuitable in distributed networks. Consequently, a hybrid framework of blockchain-based cloud-edge-end and trust mechanism were designed by the authors. The researchers inclusively used a consensus mechanism of BLS-based proof of replication (PoRep) to ensure device mutual trust and to proactively  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Citations** & **Learning Techniques** & **Attack Types** & **Security Model/Types/ Architecture** & **Contributions** & **Limitations** & **Dataset** \\ \hline \multirow{4}{*}{[74]} & ANN, RNN-LSTM, and RNN-GRU & \multirow{4}{*}{Botnet} & \multirow{4}{*}{Availability} & The models have a better result of accuracy, unlike precision and recall, particularly with smaller samples & High computation and memory usage for IIoT networks & High computation and memory usage BotIoT \\ \cline{1-1} \cline{5-8}  & & & & & smaller samples & & NF-BoT-IoT-  
prevent dynamic calculation of data replicas by the cloud server. However, verifiable delay functions (VDF) were executed in resistance to parallelization. The experimental result revealed that the scheme is low-cost-computing, communication, and storage. Despite the wider storage space provided by the cloud server, cloud computing is still being challenged with security. This security challenge is still open for research. As a result, [PERSON] et al. [82] argued for the safety of confidential information being exchanged with cloud infrastructure and instead merged blockchain and software-defined networking (SDN) techniques for enhanced cloud security within the IoT paradigm. In the proposed model, DistB-SDCloud, blockchain ensures privacy, integrity, flexibility, and scalability, while SDN improves the durability, stability, and load balancing of cloud infrastructure toward IIoTsec. In recognition of the emerging technologies that necessitate recency in research outlets, we present in Table 3 only the recent publications that execute blockchain technology toward IIoTsec.