The cloud-enabled Internet of controlled things (IoCT) envisions a network of sensors, controllers, and actuators connected through a local cloud in order to intelligently control physical devices. Because cloud services are vulnerable to advanced persistent threats (APTs), each device in the IoCT must strategically decide whether to trust cloud services that may be compromised. In this paper, we present iSTRICT, an interdependent strategic trust mechanism for the cloud-enabled IoCT. iSTRICT is composed of three interdependent layers. In the cloud layer, iSTRICT uses FlipIt games to conceptualize APTs. In the communication layer, it captures the interaction between devices and the cloud using signaling games. In the physical layer, iSTRICT uses optimal control to quantify the utilities in the higher level games. Best response dynamics link the three layers in an overall "game-of-games," for which the outcome is captured by a concept called Gestalt Nash equilibrium (GNE). We prove the existence of a GNE under a set of natural assumptions and develop an adaptive algorithm to iteratively compute the equilibrium. Finally, we apply iSTRICT to trust management for autonomous vehicles that rely on measurements from remote sources. We show that strategic trust in the communication layer achieves a worst-case probability of compromise for any attack and defense costs in the cyber layer.
black Designing trustworthy cloud service systems has been investigated extensively in the literature. Various methods, including a feedback evaluation component, Bayesian game, and domain partition have been proposed @cite_21 @cite_45 @cite_36 . Trust models to predict the cloud trust values (or reputation) can be mainly divided into objective and subjective classes. The first are based on the quality of service parameters, and the second are based on feedback from cloud service users @cite_21 @cite_49 .
@cite_36 @cite_21 @cite_49 @cite_45
2028837511 2074761227 2567766774 2791420230
In this paper, we address the cloud service trustworthiness evaluation problem, which in essence is a multi-attribute decision-making problem, by proposing a novel evaluation model based on the fuzzy gap measurement and the evidential reasoning approach. There are many sources of uncertainties in the process of cloud service trustworthiness evaluation. In addition to the intrinsic uncertainties, cloud service providers face the problem of discrepant evaluation information given by different users from different perspectives. To address these problems, we develop a novel fuzzy gap evaluation approach to assess cloud service trustworthiness and to provide evaluation values from different perspectives. From the evaluation values, the perception-importance, delivery-importance, and perception-delivery gaps are generated. These three gaps reflect the discrepancy evaluation of cloud service trustworthiness in terms of perception utility, delivery utility, and importance utility, respectively. Finally, the gap measurement of each perspective is represented by a belief structure and aggregated using the evidential reasoning approach to generate final evaluation results for informative and robust decision making. From this hybrid two-stage evaluation process, cloud service providers can get improvement suggestions from intermediate information derived from the gap measurement, which is the main advantage of this evaluation process. Consumers' feedback is a good source to help assess overall trustworthiness of cloud services. However, it is not unusual that a trust management system experiences malicious behaviors from its users (i.e., collusion or Sybil attacks). In this paper, we propose techniques for the detection of reputation attacks to allow consumers to effectively identify trustworthy cloud services. We introduce a credibility model that not only identifies misleading trust feedbacks from collusion attacks but also detects Sybil attacks, either strategic (in a long period of time) or occasional (in a short period of time). We have collected a large collection of consumer's trust feedbacks given on real-world cloud services (over 10, 000 records) to evaluate and demonstrate the applicability of our approach and show the capability of detecting such malicious behaviors. Cloud computing trust management has become an important subject in recent years. Trust management is a difficult and complicated work in cloud computing due to features such as distributed, dynamic and non-transparent environment. Since most trust management frameworks predict trust values based on received feedbacks, the validation and authenticity of feedback are highly necessary. In this paper, two new methods are presented to identify fake feedbacks. One is feedback evaluation component and the other is Bayesian game model. The feedback evaluation component is used to examine the received feedback and identify its probable fake identity. Our results reveal that the feedback evaluation component can correctly identify and rectify fake feedbacks. Bayesian game model is presented to detect malicious users and prevent their feedbacks. Simulation results coincided well with analytical results, indicating that our Bayesian game model can correctly recognize malicious user. Received feedbacks from malicious user are identified as fake feedbacks. Eventually, two new methods were compared. Cloud computing has become an important scientific computing and commercial application paradigm. Many computing resources and data exist in clouds, and face various trust issues. Malicious providers may provide poor services to users, while malicious users may give good providers unfaithful trust evaluations. Hence, it is important to detect these malicious nodes that can be providers and users. Current studies on trust management do not sufficiently address the issues of minimizing trust management overhead and maximizing the ability to detect malicious nodes. This paper proposes a new trust model and related algorithm to decrease trust management overhead and improve malicious node detection ability based on domain partition. Partitioning nodes into domains is helpful for decreasing the overhead of trust management in terms of trust storage and computation. Domain and cross-domain sliding-windows are proposed and utilized to store the most recent trust values. Then, an algorithm is designed to compute domain and cross-domain trust values for nodes, and a filter procedure is adopted to remove malicious trust evaluations and malicious nodes from a domain. Simulation results show that the proposed model and algorithm outperform two updated methods, i.e., one based on fuzzy mathematics and another based on authenticated trust and reputation calculation and management, in terms of speed and accuracy of trust computation and malicious node detection.