The connected objects are becoming omnipresent in our everyday lifes, which is shown by the rapidly raising number of Internet-connected devices predicted to billions by the year 2020. Inadequate safety, security or privacy is a barrier to large-scale deployment of IoT systems and to broad and trusty adoption of the IoT applications. Nevertheless, today the IoT struggles for duly tested/certified mechanisms and features that scale its extremely large and rapidly evolving dimension.
The project’s grand objective is to provide duly tested, benchmarked methods and tools to support the Security & Trust certification of large-scale IoT solutions using upgraded FIRE large-scale IoT/Cloud testbeds properly-equipped for Security & Trust experimentations.
3 objectives were defined to make the realization of the abovementioned grand objective possible:
#1 Upgrade FIRE testbeds for supporting large-scale IoT Security & Trust experiments, which are world-class Internet-of-Things testbeds, provided by the European Commission FIRE initiative that make possible large-scale experimentally-driven research. To do so, The ARMOUR project using feedback from all seven experiments, contributed to evolve its FIRE testbeds: IoT Lab and FIESTA-IoT. More specifically, on one hand, the IoT Lab testbed proposed new communication services for interaction between the testing tool, the experiment and the testbed (IoT Lab). This evolution specifically addressed the needs for large-scale executions and as a service that facilitates the labelling and certification process, set as the main objective of the toolbox. On the other hand, an instance of FIESTA-IoT platform was deployed and modified to be able to semantically model the datasets generated by the ARMOUR experiments. During the 2-years project duration, it was developed a set of tools to support a data-driven integration of ARMOUR datasets and benchmarks into FIESTA-IoT platform, allowing to generate experiment reports using the data being stored in the FIESTA-IoT platform.
#2 Provide experimented solutions for Secure & Trusted large-scale IoT environments. It has been done delivering a set of duly experimented and properly validated methods and technologies to enable Security & Trust in large-scale Internet-of-Things conditions. This is the reason why, ARMOUR addressed six IoT experiment contexts, leading to seven experiments:
A. Bootstrapping and group sharing procedures (EXP1)
B. Sensor node code hashing (EXP2)
C. Secured OS:
• Secured bootstrapping/join for the IoT (EXP3)
• Secured OS/ Over the air updates (EXP4)
D. Trust aware and wireless sensors networks routing (EXP5)
E. Secure IoT Service Discovery (EXP6)
F. Secure IoT Platforms (EXP7)
All over the project duration, each experiment applied the testing and certification methodology. The consortium managed to apply the methodology and the tools for the identified IoT levels: devices and data, connectivity, applications, services and platforms. Summarising, these different security technologies were evaluated using the ARMOUR systematic approach and ARMOUR toolbox.
#3 Benchmarks, framework and novel certification scheme for Secure & trusted large-scale IoT, which support the development of Security and Trusted IoT applications and setting confidence in their deployment. At the end of the project, ARMOUR project provided a certification methodology and set of tools to support the IoT security certification. It is based on an instantiation of the approach proposed by ETSI (European Telecommunications Standards Institute), using a risk assessment and the ARMOUR testing methodology. The methodology is the result of the study of the different schemes and their comparison to market needs for suitable strategy for the IoT environment. It considered the characteristics of the IoT environments, notably its high dynamics and the large-scale aspect. A proof of concept has been applied to the 7 experiments defined in ARMOUR, thus assessing the experiments identified: security aspects, on one hand, and the large-scale aspects of the tooling, on the other hand.