## 4 Limitations, Challenges, and Prospects
### Prospects of the Study  
Our research findings pointed out that the challenges in the literature and the deployed approaches foster the prospects of this SLR. As such, the prospects of this SLR are presented here. IIoT bridges the gap between intelligent machines, advanced analytics, and people at work, as it stems from endogenous requirements of industrial development and technology-driven features of Internet evolution to the delivery of an optimal smart, productive ecosystem [10; 121]. Our study, therefore, depicts how the IIoT ecosystem promises large-scale customization, dynamic optimized and real-time production flexibility, and maximized resource utilization and minimized energy consumption. However, the vulnerabilities embedded in IT/OT convergence as presented in Table 1 are yet to receive adequate attention from the researchers for their persistence, whereas the convergence is fundamental to the IIoT ecosystem [21; 22].  
According to [PERSON] et al. [8], the IT paradigm, unlike OT, of convergence has been marginalized in the research cycle of IIoTsec. Unfortunately, it is at the upper layer of IT/OT convergence. It, therefore, serves as the loophole for data-centric security, which is a threat to the IIoT ecosystem. Likewise, our broad classification unveils how architectural design loopholes require more research attention than MFC. As a result of the research gap in these two concepts, it is pertinent for the research community to fill the gap to realize the required IIoTsec. In addition to the CLAs for IIoTsec, the DL approach receives significant attention from the researchers toward IIoTsec, as depicted in Table 2. This is not unconnected to the heterogeneity concerns of the IIoT ecosystem and the unique velocity and massive features of the industrial data. Meanwhile, the evaluation of IIoT-tailored models with non-IoT-centric data are assumed to be unreliable for decision-making and generalization. Thus, instances of IIoT-centric datasets are presented, and subsequent researchers are encouraged to evaluate using IIoT-based datasets in this niche.  
Furthermore, execution time, computational complexity, and energy consumption remain the major drawbacks across CLAs. These bottlenecks have drastic effects on a heterogeneous network, like IIoT, and even make the network more susceptible to classical and quantum attacks. Thus, QM recently gained the attention of the researchers to mitigate such attacks in LSNs of the IIoT ecosystem [35]. Our findings show promising efficiency in deploying QM, although this emerging technology has some challenges as earlier highlighted owing to its infant stage of deployment [122]. In addition, the research direction shows that hybrid QCL would reinforce IIoTsec, and henceforth advance the optimization of technological operation, enterprise management, and the IIoT ecosystem [123; 124; 125]. The security capability demonstrated by QSVM, QPSO, and QNN in the related concepts of IoMT, finance analytics, material sciences, and medicine therapies over a single deployment technique is a testimony to the veritability of QCL in IIoTsec [33; 36; 101; 126; 127]. Hence, the deployment of hybrid QCL is very promising for IIoTsec. Thus, continuous research in this field is promising to proffer practicable remedies to the outstanding challenges, such as awaiting wearing-out of classical bits according to [PERSON]'s law [4], decoherence and gateinfidelity, infeasibility of non-IIoT working environment, quantum attacks, exponential rate of IoT penetration, and proposed quantum Internet [37]. Thus, the primary takeaway from the findings depicts that hybridizing quantum with DL promises better reliability for IIoTsec.