Managing Security Trade-offs in the Internet of Things using Adaptive Security

Abstract

Adaptive security can take dynamic trade-off decisions autonomously at runtime and is considered a key desirable attribute in the Internet of Things (IoT). However, there is no clear evidence that it can handle these trade-offs optimally to add value to such a complex and dynamic network. We present a scenario-based approach to recognize and evaluate typical security trade-off situations in the IoT. Using the Event-driven Adaptive Security (EDAS) model, we provide the assessment of dynamic trade-off decisions in the IoT. We have showed that an optimum trade-off mitigation response in the IoT can be automated by assessing various contextual requirements, such as the QoS and user preferences, thing capabilities, and the risk faced, at runtime. eHealth scenarios are examined to illustrate system application in IoT-based remote patient monitoring systems.