## 3 Literature Review
### IoT
#### 3.3.6 Addressing Authentication Issues in Supply Chain with Zero Trust
Power grids are designed to operate with or without SCADA, and any downtime (due to cyber-attacks on OT) can be remediated manually by bringing up SCADA. They concluded that overlaying ZTA on a control system might break the established available coordination in the control systems. Hence, implementing zero trust would neither be advisable nor support the electric power grid. A technology framework to facilitate zero trust for Industrial IoT (IIoT) was researched by [77]. The paper describes zero trust requirements for IIoT and proposes a three-stepframework for wireless IIoT. The steps include (a) creating security zones with artificial noise encoding based on physical layer, (b) industrial devices being authenticated within the security zones with physical fingerprints and (c) physical layer based key distribution to secure critical transaction flows within security zones. The authors conclude that the feasibility of the framework was demonstrated with the experiments conducted. The paper refers to zero trust five-step implementation and implements Step 1 for the protect surface by defining security zones and Step 2 by mapping transactions and securing the flows with physical layer based key distribution. The influence of emerging technologies (like artificial intelligence/machine learning, blockchain, quantum computing, etc.) on zero trust has been researched by [78]. [PERSON], [PERSON] has discussed the latest developments in network connected IoT and emerging technologies and the resultant cyber threats due to the diversification of attack vectors. The author has discussed the White House Executive Order [79] that advocated using zero trust across US federal agencies to address the new threat landscape. They have used the six pillars of zero trust (data, device, application, identity, infrastructure and network) to study the influence and use of emerging technologies to improve security. The focus of this study is the trust algorithm used in implementation of zero trust environments, which is implemented using emerging technology like machine learning based trust evaluation, Bayesian network models, graph-based trust propagation, behaviour analysis and quantum inspired algorithms. This helps achieve risk evaluation of edge devices with (a) continuous device posture assessment, (b) device identity and authentication, (c) behaviour analysis and anomaly detection, (d) micro-segmentation and access control, (e) vulnerability scanning and prediction, and (f) innovation in identity and access management. The author discusses key points for implementing zero trust systems with (a) continuous adaptive trust, (b) intent-based security, (c) zero trust data security, (d) software defined perimeter (e) zero trust edge (f) secure access service edge (SASE), (g) zero trust as code, (h) AI-driven zero trust and (i) quantum-safe zero trust. The author concludes that as a part of this research they will deep-dive into each focus area and conduct experiments with a variety of use cases to further provide recommendations and assess the impact of these advancements on the zero trust posture of organisations. In summary, zero trust has been used to address various security issues identified in IoT devices and environments. The following conclusions arise from the study of the literature:  
* Addressing privacy issues in interconnected IoT devices has been researched, resulting in authors advocating zero trust for resiliency and for authentication [44; 45; 46]. * Reducing the risks of malicious access requests has been studied by authors, which has resulted in using zero trust to address context aware access control for IoT devices by calculating risk and trust values of the access requests made by entities [47; 48]. * Requests for IoT devices from malicious actors have been a prevalent problem, as studied by various authors.