Trust in data collected by and passing through Internet of Things (IoT) networks is paramount. The quality of decisions made based on this collected data is highly dependent upon the accuracy of the data.
For example, Man-in-the-middle attacks of health monitoring systems, e.g., Fitbit, can lead to misdiagnosis and dangerous reactive treatments. Hacked-into sensors, edge nodes, and network elements can cause system instability. Inaccurate humidity sensors in aggregate can destroy produce
- Minor inaccuracies and not understanding the context can add up to major mistakes.
IoT devices are "in the wild" (i.e., in hostile environments) and hard to secure and maintain (at scale)
- IoT based DDOS attack
- Collusion attacks can be costly
Our project proposes a systematic method for scoring and managing trust.
- Framework for proactively testing system and data integrity.
- Tracking data throughout the entire network-evaluating provenance and reliability & testing risk via device/network controls
- Leverage CA work on API-M, IAM, and Step-up Authentication