Page 84 - Trust in ICT 2017

1 Trust in ICT

The cooperativeness represents the level of the social cooperation from the trustee to the trustor. The higher
cooperativeness means the higher trust level. A user can evaluate the cooperativeness of others based on
social ties and select socially cooperative users.
The community-interest represents whether trustor and trustee have close relationship in terms of social
communities, groups, and capabilities. Two entities with a degree of high community-interest have more
opportunities in interacting with each other, and thus can result in higher trust level.

The experience of trustor A to trustee B in particular context ‘c’ (service C) is based on the track record of
previous interaction. If the interaction is successful then, experience value is +1, in case of failure it is -1. The
record of the successful and unsuccessful interactions is valuable information for trust judgment.

The detail calculations of the three TAs Honesty, Cooperativeness and Community-Interest are presented in
[85] whereas the TA Experience is achieved from the interaction record conducted by Trust Agent. By taking
these trust properties, our trust service platform will be able to deal effectively with certain types of malicious
behaviour aimed at misleading other entities.
The Human-to-Object knowledge depends on both service and object; and can be calculated using sufficient
information provided from the service with appropriate reasoning methods and machine learning technique.

8.5 Autonomic trust management

The future ICT environment integrates a large amount of everyday life devices from heterogeneous network
environments, bringing a great challenge into trust, security, and reliability management. In doing that, smart
objects with heterogeneous characteristics should cooperatively work together. It is a known fact that the
devices particularly in IoT very often expose to public areas and communicate through wireless, hence
vulnerable to malicious attacks [89] [90] [91]. Migrating IoT application specific data into the Cloud offers
great convenience, such as reduction of cost and complexity related to direct hardware management [92]
[93] [94]. However, to evaluate the trustworthiness of their systems cannot use only the past experiences,
since the novel autonomic systems nowadays are highly dynamic and the behaviors are unpredictable. These
restrictions are detrimental to the adaptation of Trust Management Systems to today’s emerging IoT
architectures, which are characterized with autonomic and heterogeneous nodes and services.
Clouds or cloud computing has picked up many researchers’ attention, as such it is being a part of IoT.
Undoubtedly, trust management is the most challenging issues in emerging cloud systems where millions of
services, applications and nodes deployed together under a single umbrella to serve each other [95].
Together with the current dynamism of the systems and the autonomous users’ behavior, the latter task has
been too complicated [96]. In reality, autonomic trust management is hard to be realized because the cloud
of things is hard to control due to the scale of deployment, their mobility and often their relatively low
computation capacity [97] [98]. As a result, the trust manager itself should be adaptive to the autonomic
conditions posed by the system.
This sub-section shows a framework for autonomic trust management based on Monitor, Analyse, Plan,
Execute, Knowledge (MAPE-K) feedback loop to evaluate the level of trust in an IoT cloud ecosystem. Even
though many research activities were carried out in the scope of autonomic trust management, non of them
have addressed how an integration between IoT and cloud would work. It is necessary to utilize MAPE-K
feedback control loops to enhance consistency of the system while improving robustness and scalability with
the introduction of cloud concepts.

76