## 3 Key Findings
### RQ2: What Kinds of Learning Algorithms Are Being Deployed Towards the Security of IIoT?
#### 3.2.2 Deep Learning-Based Technique
Also, as discussed in Section 3.1.2, safe data transmission is an issue in an IIoT ecosystem due to its multi-nodal architecture. Therefore, to ensure safety and strict coordination, and to deter key leakages, [PERSON] et al. [78] proposed a ciphertext policy attribute-based encryption mechanism scheme for secure access control. The authors utilized a hybrid cloud infrastructure for encryption and decryption to minimize the computational overhead effect. Likewise, [PERSON] and [PERSON] [79] also proposed a DL-based energy-efficient optimal RMC-CNN model to secure data transmission and anomaly detection in IIoT. In the authors' approach, RMC-CNN was used to distinguish types of attack--sybil and DoS--in the network while a multi-scale grasshopper optimization scheme was implemented to optimize the network model. Data encryption was performed using a dynamic honeypot encryption algorithm, which was thereafter securely transmitted into the cloud for storage. The validation of the scheme was conducted using the power, loop sensor, and land sensor dataset. When its throughput, delay, and detection rate analysis were compared with existing techniques, the scheme had a better outcome of accuracy, precision, recall, and F1 score. Another defense approach against DDoS attacks in an IIoT ecosystem was presented in [52]. The authors deployed multi-point synergic capability at the edge to protect IIoT devices from attackers. The authors adopt two DL mechanisms, LSTM-Attention network and 1D CNN architecture. While the former differentiated benign traffic from attack, the latter categorized and detected the attacks. The proposal was validated using the DoS2019 dataset. Both the DL methods achieved high-performance metrics in precision, recall, and F1 score. However, the 1D CNN-based method had a better result. Moreover, in a continuous effort of the research community to achieve safe data transmission in the IIoT environment, a chaotic map and resource-efficient AE scheme (ASCON)-based authentication framework for IIoT (CMAF-IIoT) was proposed in [39]. The proposed framework [39] was to ensure seamless communication between the users and smart devices via local authentication on the devices and the establishment of secure session keys with the devices. The evaluation of the scheme revealed its low-cost communication, computation, and storage with resource friendly and improved security measures against privileged insiders, passive attacks, and MITM attacks. Similarly, an approach toward secure intranodal device communication in the IIoT ecosystem was also conceived by the authors in [14]. The authors, therefore, proposed a memristive hyperchaotic system-based complex-valued artificial neural network (ANN) synchronization to ensure secure coordination and synchronization of session key switch-over in a hyperchaotic environment like IIoT. The authors generated secure input for ANN synchronization using the proposed scheme alongside complex-valued parameters for ANN. Such hard weight values of ANN were to make the attacker's guess difficult. However, the absence of an optimization technique for the weight value would likely cause a long synchronization time. Having provided answers to the first segment of RQ2, we concisely present learning approaches toward IIoTsec, the models used, limitations, and the datasets for validation in Table 2.