# Analysis of Security Issues and Countermeasures for the Industrial Internet of Things
## 5 Discussion and Future Research Direction
It provides a distributed and auditable ledger that holds the various blocks of previous transactions where the records within a blockchain are compounded by mathematical algorithms (called consensus), and whose data are shared between the various peers within the network [181]. In other words, blockchain can be seen as a distributed database where the information is verified with the previous data. That said, in a blockchain network, the previously stored information cannot be erased. Fundamentally, every node in the network has the same ledger, which ensures a complete consensus from all nodes for the transactions in the blockchain. Blockchain can enrich the IIoT systems by providing a platform for sharing information in a trustless environment, where information is reliable and translations are traceable, which provides the ability to identify sources at any time. That said, the use of blockchain can track, coordinate, and perform transactions for a large number of devices without the need for a centralized (trusted) authority, which complements the IIoT in various ways including reliability, security, and scalability [182]. Zero Trust is another strategic initiative that has the potential to secure an organization's network architecture (including expensive data, assets, applications, and services) without replying to a dedicated trust architecture. In other words, the zero-trust strategy provides restricted access control only to the authorized users and does not trust anyone by default [183; 184]. Furthermore, blockchain supports fully distributed access control with a high degree of trust, integrity, and resiliency that are immensely important for securing an IIoT system [185; 186]. Machine Learning (ML) is another avenue with enormous potential to secure IIoT infrastructure from threats and attacks [187; 188]. ML uses past behaviors and helps identify \begin{table}
\begin{tabular}{c c c} \hline \hline
**IIoT Architectural Layer** & **Security Issues** & **Potential Countermeasures** \\ \hline \multirow{8}{*}{Application} & Privacy & A privacy-preserving data mining technique that helps to minimize sensitive data disclosure can be employed [163]. \\  & Heterogeneity of Networks & A two-way authentication scheme can be used that absorbs the existing Internet standards, specifically the Datagram Transport Layer Security (DTLS) protocol [164]. \\  & Anonymity & An identity-based encryption scheme, e.g., [165] can be used that helps to detect anonymous communications. \\ \cline{2-3}  & Heterogeneous Infrastructure & A distributed capability-based access control (DCapBAC) framework for IIoT devices can be employed [166]. \\ \cline{2-3}  & Cascading Services & A capability-based delegation model for the federated networks can be employed [167]. \\ \cline{2-3}  & Emergence of Resources & Cryptography-based security mechanism, for instance, that involves public key infrastructure (PKI), can be used [168]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._future patterns (i.e., analyze the patterns and learn from them and generate efficient models with changing parameters) that eventually help detect attacks. Importantly, ML can detect the type of attacks, including both known and unknown attacks. Some aspects of IIoT security that can be improved using ML are securing 5G-driven IIoT applications [189], malware defense [190], and false data injection attack detection [191]. However, there are several challenges in this area that need to be addressed in the future. We list a few of them as follows:  
* Training datasets for ML-based solutions: generating training datasets with sufficient numbers of possible attacks and benign traffic is challenging for ML algorithms in IIoT security. In addition, high-quality training data are required for ML methods, as the noisy nature of IIoT data can affect the performance of ML-based intrusion detection. Deep Learning (DL) methods can be used for large-scale, heterogeneous, and noisy datasets in IIoT networks [192].