Autonomic Trust Management Research Articles

AutoTrust: A privacy-enhanced trust-based intrusion detection approach for internet of smart things

Internet of Cloud Things (IoCT) is a new era of technology introduced as an extension to Internet of Things (IoT). IoT itself is a complex and heterogeneous network that allows access to the cloud that raises numerous privacy and security challenges. When devices access the cloud for storage or services that are provided by other cloud services, it is important to identify and eliminate malicious service providers. Different encryption approaches have been proposed, however, these cause more energy consumption. In the IoT scenario, several nodes are located in a remote area where a consistent supply of energy is impossible. To address this challenge, a recurrent neural network (RNN)-based autonomic trust management approach, named AutoTrust, is proposed in this paper, which can predict the malicious behaviors of nodes and eliminate them. The proposed mechanism maintains a set of standards provided by the trustors offering an additional tier of security. A novel dataset consists of 70,000 values has been utilized to train and test the model. The AutoTrust is evaluated and compared with the existing mechanisms, where the results show that the proposed mechanism can provide better privacy and security.

Trust Models in Grid Computing: A Review

Background:Grid computing relates to a pool of resources to be shared by users in Grid Environment. Security of the resources from users and vice-versa is a significant issue. This is where the notion of trust comes into existence. A number of researchers have proposed models for evaluation of trust in grid computing, but they fail to spot one or the other parameters for trust evaluation. The essence of trust models in grid computing is that they offer autonomic trust management. An autonomic trust model has been patented by Z. Yan and C. Prehofer in 2009. Another patent was published by Anna University in 2010 to evaluate the trustworthiness of a resource provider in Grid environment.Objective:This paper firstly focuses and illustrates these essential parameters. Based on these parameters, further, a comparison of some existing models for trust evaluation is shown. Finally, common parameters missed out by various models have been highlighted giving way for improvements of Trust model.Methods:A Trust evaluation model has been proposed by us previously based on a number of real-world trust evaluation parameters. This model sees trust as a three-dimensional entity. Trust is based on Dempter Shafer’s theory in which trust is calculated mathematically.Results:Software trust needs to be calculated mathematically. There are a large number of real-world parameters that need to be included for evaluating trust.Conclusion:As trust models in research are based on simulation techniques, so it is important to include real-world factors that affect trust value of one entity on other. Some of those parameters, missed by most of the models have been identified for inclusion in future trust models.

Cognitive security: securing the burgeoning landscape of mobile networks

The rapid proliferation of personal wearable as well as embedded devices point to the emergence of networks of unprecedented size and complexity in the near future. Unfortunately, traditional network security solutions fall short of addressing the unique security requirements of the emerging environment given their general emphasis on administratively managed, preconfigured security context and strong physical security mechanisms. To cope with the security challenges of this emerging environment, novel cognitive-inspired security architectures have been proposed that emphasize dynamic, autonomous trust management. Cognitive security systems take advantage of sensing and computing capabilities of smart devices to analyze raw sensor data and apply machine learning techniques to make security decisions. In this article, we present a canonical representation of cognitive security architectures and examine the practicality of using these architectures to address the security challenges of rapidly growing networks of mobile/embedded autonomous devices including the ability to identify threats simply based on symptoms, without necessarily understanding attack methods. Using authentication as the main focus, we introduce our canonical representation and define various categories of contextual information commonly used by cognitive security architectures to handle authentication requirements, and highlight key advantages and disadvantages of each category. We then examine three grand challenges facing the cognitive security research including the tension between automation and security, the unintended consequences of using machine learning techniques as a basis for making security decisions, and the revocation problem in the context of cognitive security. We conclude by offering some insight into solution approaches to these challenges.

Autonomic Trust Management in Cloud-Based and Highly Dynamic IOT Applications

In this paper, we propose an autonomic trust management framework for cloud based and highly dynamic Internet of Things (IoT) applications and services. IoT is creating a world where physical objects are seamlessly integrated in order to provide advanced and intelligent services for humanbeings in their day-to-day life style. Therefore, trust on IoT devices plays an important role in IoT based services and applications. Cloud computing has been changing the way how provides are looking into these issues. Many studies have proposed different techniques to address trust management although non of them addresses autonomic trust management in cloud based highly dynamic IoT systems. To our understanding, IoT cloud ecosystems help to solve many of these issues while enhancing robustness and scalability. On this basis, we came up with an autonomic trust management framework based on MAPE-K feedback control loop to evaluate the level of trust. Finally, we presents the results that verify the effectiveness of this framework.

Autonomic Trust Management for a Component-Based Software System

Trust plays an important role in software systems, especially component-based systems in which components or their environments vary. This paper introduces an autonomic trust management solution for a component-based software system. We propose an adaptive trust control model to specify, evaluate, establish, and ensure the trust relationships among system entities. This model concerns the quality attributes of the entity and a number of trust control modes supported by the system. In particular, its parameters can be adaptively adjusted based on runtime trust assessment in order to reflect real system context and situation. Based on this model, we further develop a number of algorithms that can be adopted by a trust management framework for autonomic management of trust during component execution. We verify the algorithms' feasibility through simulations and demonstrate the effectiveness and benefits of our solution. We also discuss the issues for successful deployment of our solution in a component software platform.