## 4 End to End Security Analysis on the Proposed IIoT Security Architecture
### Data Processing Layer
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multirow{2}{*}{**Technology Standard**} & \multirow{2}{*}{**Features/ Functionalities**} & \multicolumn{3}{c}{**Analysis of Security Requirement**} & \multirow{2}{*}{**Deployed Security Countermeasures**} & \multirow{2}{*}{**Ongoing Challenges and Future Research**} \\ \cline{3-4} \cline{5-8}  & & **C** & **I** & **A** & **V** & \\ \hline \multirow{6}{*}{
\begin{tabular}{} \end{tabular} } & Radio Waves2.4 GHz & & & & Provides message confidentiality with a 256-bit encryption algorithm, uses a 16-byte hash function for message integrity. & \\  & Range & 4.6 km & & & & 256-bit encryption algorithm, uses a 16-byte hash function for message integrity. & \\  & Network topology: star & & & & subsequently meets the FITS 140-2 Level 2 subsequently meets the FITS 140-2 Level 2 encryption standards (e.g., tamper-evidence against unauthorized physical access, role-based authentication, etc.) & Availability and assurance mechanism \\  & Application: smart & & & & & \\  & transportation, tracking, smart building & & & & & \\  & smart building & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cont._Data-at-Rest Data-at-rest refers to the data being stored in persistent storage such as a disk file. Conventional data-at-rest protection approaches include installing tamper-resistant hardware in third-party service providers, full-disk encryption, database-level encryption, table level encryption, and application-level encryption. Data are encrypted in the application layer before being inserted into the database. For instance, TrustDB and CipherBase provide data-at-rest protection based on the co-design of hardware and software [110]. * Data-in-Transform Data-in-transform indicates that data is subjected to various means and manipulation methods, including performing query, sorting, mathematical operations, statistical analysis, and other functions on data to produce meaningful output. Protection of data-in-transform in IIoT is critically vital as their necessities in supporting real-time data analytics. Most of the recent big data processing service providers are still inadequate to support confidentiality during data transformation. While conventional security mechanisms are limited to protect data-at-rest and data-in-transmit, the recent advancement of homomorphic encryption algorithms can be used to ensure the security of data-in-transform. Homomorphic encryption is a data encryption algorithm that works similar to a conventional data encryption algorithm, however, with the added capability to perform computation over encrypted data. Therefore, homomorphic encryption can serve as a comprehensive data protection solution to protect real-time analytics in IIoT. Recently, homomorphic encryption has been commercialized and released. Exiting products include CryptDB, MrCrypt, Crypsis and computing on masked data (CMD). CryptDB focuses on protecting MySQL query, MrCrypt supports MapReduce operation, Crypsis aimed for high-level data flow language such as Pig Latin, and CMD protecting NoSQL environment [109]. However, all of them are constructed based on a partial homomorphic encryption scheme, which can support simple arithmetic operation either additive or multiplicative homomorphism, still inadequate to enable artificial intelligence (AI) capabilities and machine learning algorithms. Most of these solutions lead to an increased data storage size (approximately 3.76 times in CryptDB), communication cost and bandwidth. A more efficient homomorphic encryption algorithm is needed to provide end-to-end protection for real-time data analytics in the IIoT data processing layer.