ResIoT: An IoT social framework resilient to malicious activities
Giancarlo Fortino, Fabrizio Messina, Domenico Rosaci, Giuseppe M. L. Sarnè
Abstract
The purpose of the next internet of things (IoT) is that of making available myriad of services to people by high sensing intelligent devices capable of reasoning and real time acting. The convergence of IoT and multi-agent systems (MAS) provides the opportunity to benefit from the social attitude of agents in order to perform machine-to-machine (M2M) cooperation among smart entities. However, the selection of reliable partners for cooperation represents a hard task in a mobile and federated context, especially because the trustworthiness of devices is largely unreferenced. The issues discussed above can be synthesized by recalling the well known concept of social resilience in IoT systems, i.e., the capability of an IoT network to resist to possible attacks by malicious agent that potentially could infect large areas of the network, spamming unreliable information and/or assuming unfair behaviors. In this sense, social resilience is devoted to face malicious activities of software agents in their social interactions, and do not deal with the correct working of the sensors and other information devices. In this setting, the use of a reputation model can be a practicable and effective solution to form local communities of agents on the basis of their social capabilities. In this paper, we propose a framework for agents operating in an IoT environment, called ResIoT, where the formation of communities for collaborative purposes is performed on the basis of agent reputation. In order to validate our approach, we performed an experimental campaign by means of a simulated framework, which allowed us to verify that, by our approach, devices have not any economic convenience to performs misleading behaviors. Moreover, further experimental results have shown that our approach is able to detect the nature of the active agents in the systems (i.e., honest and malicious), with an accuracy of not less than 11% compared to the best competitor tested and highlighting a high resilience with respect to some malicious activities.