## 3 Key Findings
### RQ2: What Kinds of Learning Algorithms Are Being Deployed Towards the Security of IIoT?
#### 3.2.2 Deep Learning-Based Technique
\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
[31] & RF and Bat Algorithm & Command injection, backdoors, and SQL injection & Command injection, classification & Two bioinspired-based feature selection algorithms were trained to reduce computational time and false alarm rate in IIoT & The ranking of attack traffic from the normal traffic was not by DL & WUSTL-IIOT-2021 \\ \hline
[14] & Hyperchaotic-guided PRNG & MITM, impersonation, etc. & Memristive hyperchaotic system-based complex-valued Artificial Neural Network & Use of hyperchaotic environment to swiftly assess the ANN’s coordination and synchronization of session-key switch over & Longer time of sync of ANN’s parameters, and no optimization technique to optimize weight values for sync’s time effects & Longer time of sync of ANN’s parameters, and no optimization technique to optimize weight values for sync’s time effects & \\ \hline
[39] & Hash algorithm, chaotic map, and ASCON & Password guessing, impersonation, replay attacks, and MITM & Chaotic map-based authentication framework (CMAF-IIoT) & Reliable communication between smart devices and users. Efficient resource and enhanced security with low storage, communication and computational costs. & Only 3 participants and 3 messages were considered & Custom \\ \hline
[75] & MLs and DLs & Backdoor, DDoS, DoS, Mirection, MITM, XSS, Password, Ransomware, Scanning & Classification & Achieved an accuracy rate of 99.99\%, and a reduced error rate of 0.001\% for binary accuracy rate of 99.98\% and a reduced error rate of 0.016\% for multi-class classification & Complexity for IoT devices & DoS2019 \\ \hline
[52] & LSTM and 1D CNN & DDoS & Availability & Both approaches achieved significant precision, recall, and F1 score making it protect devices from DDoS attacks & Complexity for IoT devices & DoS2019 \\ \hline
[79] & Robust Multi-cascaded CNN & Sybil and DDoS. & Dynamic honey spot encryption algorithm for data (key) & Secure data transmission scheme in power, loop, and land IIoT & Computational complexity in comparison with others & Loop, power, and land sensors \\ \hline
[76] & Auto Encoders AE and CFRPNN & Zero-day vulnerabilities & Zero-day notDistrial Internet-of-Things (PIGNUS). & Offers better classification of normal and abnormal accuracy. Compared to existing models, 20\% improved FPR, 10\% better recall, 10\% better in precision & Alleriod storage space. Response time still needs to be upgraded & NA \\ \hline
[3] & 3 smart contracts—TIC, URC, and MC. Blockchain & Malicious attack & Access control. Lightweight PQE algorithm-NTRU preserves user privacy during the registration & If offers an enhanced accuracy, precision, and detection rate for Edge-Based IIoT intrusions & Computational complexity & Bot-IoT dataset and NSL-KDD \\ \hline
[9] & KNN and PCA & Intrusions & Lightweight PK-IDS framework & Distinguish malicious actions from IIoT-driven IICS networks in real time. 97.95\% and 97.62\% accuracy for the dataset respectively & Gas pipeline and and USWNB-I5 \\ \hline
[73] & LSTM & DoS, Reconnaissance, Exploits, Fuzzers, NMR, & Deep- autoencoder based IDS & Distinguish malicious actions from IIoT-driven IICS networks in real time. 97.95\% and 97.62\% accuracy for the dataset respectively & Gas pipeline and and USWNB-I5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Learning models approach for IIoTsec. (NA: Not applicable).