## 3 Key Findings
### RQ2: What Kinds of Learning Algorithms Are Being Deployed Towards the Security of IIoT?
#### 3.2.2 Deep Learning-Based Technique  
Likewise, [PERSON] and [PERSON] [71] implemented a long short-term memory (LSTM) algorithm to checkmate the discovered DoS attacks in a distributed fog environment of an IIoT firm. Before the deployment, the firm had been challenged with an attenuated network performance and inefficiency of the network entities. The proposed technique was validated with two datasets--ISCX and AWID. When its performance was compared with the LR algorithm, the proposed technique had better accuracies of 99.91% and 98.22% on ISCX and AWID datasets, respectively, as well as acceptable precision of attacks' detection. However, LR has a record of shorter training times than the LSTM technique. Additionally, [PERSON] and [PERSON] [72] combined two DL algorithms, RNN and LSTM, as IDS to address brute force attacks. DL algorithms were used for malware detection, while network functions virtualization (NFV) technology was adopted as malware diffusion resistant in a heterogenous IoT ecosystem. The authors validated the proposed technique using the UNSW-NB15 dataset. Similarly, [PERSON] et al. [73] implemented an LSTM autoencoder to uniquely distinguish several malicious actions and reduce false alarms in IIoT-driven IICS networks. GP dataset was collected for anomaly detection in IICS networks, while the UNSW-NB15 dataset was for anomaly detection from exterior networks. The malicious actions discovered in the ecosystem include DoS, reconnaissance, exploits, fuzzers, and NMRI. Accuracy values of 97.95% and 97.62% were achieved for GP data and the UNSW-NB15 dataset, respectively. The recurrence of DoS, DDoS, and botnet attacks in an IIoT ecosystem became more of a concern in the work of [PERSON] et al. [74], which, therefore, inspired the authors to proffer security remedies using hybrid DL algorithms consisting of RNN-LSTM, RNN-GRU, and ANN. Meanwhile, the three models have a low-performance percentage of recall and precision in classifying the assaults with a smaller number of instances until an under-sampling of the majority class was carried out. Of the models, ANN achieved the highest accuracy performance of 99%, while RNN-GRU was able to excellently detect DoS and DDoS attacks targeting HTTP protocol, even with minimal samples. All three models were validated with the Bot-IoT dataset. Moreover, the hybrid approach of ML and DL techniques has been explored several times to identify and mitigate cyberattacks in IIoT networks. [PERSON] et al. [75] conceived the idea and implemented it. The authors henceforth considered ML-based IDSs such as ensemble bagging, decision tree (DT), and KNN for attack prediction and classification. For the precise identification of the attacks in the IIoT environment, DL-based approaches such as LSTM, bidirectional LSTM, and GRU were trained to identify the cyber-attacks. SVD was employed for feature engineering while SMOTE was used to address imbalanced data challenges. The study was evaluated on the ToN-IoT dataset for binary and multiclass classification. In addition, [PERSON] et al. [76] also proposed a PIGNUS model--a hybrid AE and Cascade Forward Back Propagation Neural Network (CFBPNN) algorithms--againzero-day vulnerabilities in an IIoT ecosystem. The proposed technique was validated with five publicly available datasets. The obtained result revealed an accuracy of above 95% and a zero percent false rate in detecting multi-class attacks. Meanwhile, in 2022, the work [77] implemented an IDS using three techniques CFBPNN, CFS, and NARX against botnet attacks. Unlike the autoencoder (AE) used for feature selection and improved detection rate performance in 2023, CFS was used. While NARX was utilized as a time-series technique to identify high-impactful elements in the target class. The proposed technique was also tested on different five datasets--NF-UNSW-NB15, NF-CSE-CIC-IDS2018, NF-ToN-IoT, and NF-BoT-IoT for validation. When the performance outcome was compared with various existing NN models, the proposed technique was better in accuracy, F1 score, and precision.