# A Survey on Cyber Risk Management for the Internet of Things
## 4 Cyber Risk Assessment for IoT Survey Results
### IoT Cyber Risk Identification
#### 4.1.3 Identification of Threats  
Threats are circumstances with the potential to adversely impact organisational operations assets, or individuals using attacks that allow for \"unauthorised access, destruction, disclosure, modification of information, and/or denial of service\" [79] by exploiting vulnerabilities. IoT threats are the events that have the potential to adversely impact IoT assets and users [15]. To identify threats in IoT systems, it is necessary to discover their sources and assess their potential impact. IoT risk management frameworks offer several ways to achieve this, including the use of established threat modelling methods, the development of new threat models, and analysis of attack use cases. Threat-based risk assessment for IoT involves evaluating potential risks associated with IoT devices by analysing and modelling potential threat scenarios. This approach is an essential part of the overall IoT risk assessment process, helping to identify and prioritise potential risks. As noted in [15], this approach involves modelling, developing, and analysing potential threats to determine the overall risk posed by an IoT device or network. There are several effective threat modelling methods available, such as STRIDE [80] and LINDDUN [81]. Microsoft's STRIDE threat model is the most widely used in IoT cyber risk management frameworks to identify and quantify potential threats. It divides threats into six categories, namely spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege [80]. This model has been extensively referenced in recent research on IoT security, including studies on threat analysis [49; 59; 69; 70], threat vectors [42], and attack surfaces [44]. Therefore, utilising the STRIDE threat model can provide a solid foundation for comprehensive IoT security risk assessment and management. The issue with using STRIDE is that, while it is good for security risks, privacy risks are often not exhaustive, making it insufficient in places where privacy is of the utmost importance. LINDDUN targets the modelling of privacy-related threats, these being linkability, identifiability, nonrepudiation, detectability, disclosure of information, unawareness, and noncompliance [81], while DREAD (damage, reproducibility, exploitability, affected users, discoverability) [82] allows for the comparing and prioritisation of threats using a rating. Since IoT poses a larger threat to privacy alongside containing attacks on sensors and actuation, modifications may be needed to capture all threats that could be high risk. To overcome this, [PERSON] et al. [71] simulate their proposed framework using STRIDE, DREAD, and LINDDUN, using LINDDUN to focus on privacy risks. [PERSON] et al. [73] use the STRIDE and DREAD models to find threats and attack potentials of wearable Internet of Medical Things devices, while [PERSON] et al. [44] use both models in the context of smart cities. There are various methodologies available for identifying and mitigating cyber threats, and one such approach is the OCTAVE Allegro methodology as discussed by [PERSON] and [PERSON] their study [41]. This methodology includes a dedicated phase for identifying potential threats, which involves identifying areas of concern and creating threat scenarios to better understand the various cyber threats that could target smart home data. Meanwhile, [PERSON] et al. [51] perform threat modelling using analysis from the OWASP IoT Top 10 project as a way to identify the threats. [PERSON] et al. [66] use anomaly behaviour analysis to identify behaviours that deviate from normal operations, with anomaly behaviour being a threat to IoT systems, with this behaviour being characterised by variables such as hardware configuration and system memory. [PERSON] et al. [38] propose ASRAAaS (an ArchiMate-based security risk assessment as a service model), which uses the ArchiMate modelling language. The model analyses the potential threats for IoT systems using vulnerabilities which are assessed within attack scenarios.