## 3 Literature Review
### IoT
#### 3.3.5 Addressing Implicit Trust with Zero Trust
SADAC is based on the zero trust principle of granting access to resources once the requestor has been verified. SADAC makes dynamic decisions based on monitoring using a machine learning model and detection of anomalies. Security profiles for users and devices are built using a feature generator and an estimator module. Essentially, the authors use a machine learning model to identify features of users and devices and, using the estimator, model user and device behaviour. SADAC then identifies anomalies between expected behaviour and actual observed behaviour. If an anomaly is within the accepted limits, access is granted, or else it is denied. The authors concluded that SADAC is a viable option for IoT devices on wireless corporate environments. The authors of [51] discuss critical infrastructures like smart grids, Industrial IoT and other physical systems and their associated security requirements. They consider NIST's definition of ZTA [5] and propose that the principles of ZTA are authentication and authorisation, which are considered as key principles, and discuss the role of implicit trust during the granting of access request by an entity to a resource. Based on their research, they propose that the authentication process is limited at the login stage. Once the entity is authenticated, the entity gains access within the environment. To address this implicit trust the authors proposed continuous authentication of device-to-device (D2D) communication using their proposed protocol called Lightweight Continuous Device-to-Device Authentication (LCDA). The protocol operates in two phases: (a) authentication to ensure that the devices mutually authenticate each other in a continuous manner and (b) continuous authentication to ensure that the devices do not talk to any unauthenticated devices. An informal security analysis of the LCDA protocol is performed and a formal security analysis of the protocol using Scyther Cremers [68] is conducted. They conclude that LCDA addresses both mutual authentication and continuous authentication phases with a channel state modification using a refreshed dynamic secret key. Attacks to secure D2D communication are addressed with an adaptation of a light-weight tuneable linear function. Conformation to security properties, such as integrity, confidentiality and resistance to protocol, is evaluated using the Scyther tool. Perimeter-based security and its security concerns for cloud environments are studied by [69]. The authors discuss addressing these concerns with zero trust architecture as described by NIST [5]. To understand the current state of cloud security concerns, the authors survey the literature that discusses architecture and technical features of zero trust architecture. They discuss the concepts of zero trust architecture and compare them with traditional security. Their description of zero trust architecture is centred around the core concept of origination of network threats being both internal and external. Thus, any access to the network is to be granted by a process of verification at a designed interval of time. They discuss zero trust maturity and comparative analysis of zero trust technologies as applicable to cloud network. They conclude by saying that zero trust offers case specific solutions to security issues that are applicable to network in cloud environments. The authors of [70] studied using hardware device fingerprints to create identities for IoT authentication. The authors proposed a framework using a radio frequency-based device that generates identity using a Convolutional Neural Network (CNN). The framework includes the constraints that are related to resources of IoT devices. The hardware signature is captured as the Double-Sided Envelope Power Spectrum (EPS). The hardware signature includes only related device information. The framework supports one of the zero trust principles related to the authentication of devices.