With rapid development and increasing complexity of computer and communication networks, user requirements for trust in open, dynamic, heterogeneous, mobile, wireless, and distributed computing environments are becoming more demanding. As useful and innovative technologies, trusted network computing has been attracting researchers with significant attentions. The accepted papers in this special issue are devoted to the advanced developments and research addressing related theoretical and practical aspects on trusted network computing, and the contents are built on analytical modeling, experimental and simulation studies. The contributions of these papers are outlined in the succeeding text.
With wide and close Internet accessibility, smart phones inevitably suffer security threats and information security management issues. Jang, Chang, and Tsai investigate the information security related trust issues through a user-based empirical study with the aim to study the relationships between smart phone users' computer literacy, network literacy, knowledge on mobile phone viruses, and trust in information security management principles.
Users in pervasive computing environments (PCEs) expect to access network resources and services at anytime and at anywhere, bringing significant convenience to the people's daily life. However, PCEs can lead to serious security risks and problems with access control, because these resources can be accessed by anyone with a mobile device. Chen, Wang, and Deng propose a semantic distance-based trust model to handle the security challenges emerging in PCEs. The semantic distance between entities and between trust categories is adopted to calculate the trustworthiness more precisely. All entities in PCEs make independent decisions, which can maximize their own profit with the proposed trust model. The simulation experiments show that the presented model is able to increase the interaction success ratio between entities.
The context factors are important for trust evaluation in e-commerce environments. Zhang, Wang, and Zhang target the contextual transaction trust problem and present a trust vector to outline the reputation profile of a seller. It aims to identify the context imbalance problem in forthcoming transactions that can cause a huge monetary loss for victim buyers. The authors then propose three new approaches to contextual transaction trust computation in e-commerce environments and show the merit of these schemes under different conditions via experiments based on both eBay datasets and large-scale synthetic datasets.
Reputation models in mobile ad hoc networks are confronted with the problem of strategically reporting dishonest recommendations given by a selfish group whose purpose is to maximize its own benefit. To solve this problem, Zhou et al. propose a truthful and group strategy-proof reputation mechanism based on cooperative game theory. Simulation results show that using the total compensatory payments function, which is not related to the cost values, the proposed mechanism is group strategy proof.
Radio frequency identification systems have become popular in identifying an object without the requirements of physical contact and line of sight. However, the wireless communication channel between the tags and readers is not secure and can easily be attacked by all kinds of adversaries. Based on the Dan Boneh's aggregate signature algorithm, Li, Wang, and Zheng present a fast radio frequency identification batch detection protocol to quickly authenticate whether the information is attacked when transmitting from tags to readers. The simulation results show that the proposed algorithm has better performance in terms of computation performance, storage performance, and secure performance than other related algorithms.
Traffic anomalies caused by distributed denial-of-service attacks are major threats to both network service providers and legitimate customers. Liu et al. focus on early detection of traffic anomalies caused by denial-of-service attacks in light of analyzing the network traffic behavior. The artificial neural network and support vector machine subject to the performance metrics are employed to predict and classify the abnormal traffic. The experimental results demonstrate that the developed mechanism can effectively and precisely alert the abnormal traffic within short response period.
Social networks usually contain personal sensitive information. Preserving privacy in the release of social network data becomes an important concern. Liu et al. propose k-obfuscation to protect profiles against graph property-based attacks and develop a general framework for obtaining k-obfuscation. Extensive experiments on real datasets prove the satisfactory performance of the proposed methods in terms of privacy protection, efficiency, and practical utilities.
The prevalence of social networks has raised the concern for individual privacy leakage. Yang et al. design a secure and high utility privacy preserving model, called AK-Secure, to prevent node identity attack, path length leakage, and edge leakage effectively. Based on the AK-Secure privacy preserving model, the authors propose a graph anonymous algorithm to minimize information loss and guaranteeing high data utility. Extensive experiments on real datasets show the security and effectiveness of the proposed AK-Secure privacy preserving model, and the high data utility of the released anonymous graph.
Memory bugs are most common and dangerous software vulnerabilities, which can be used by attackers to cause software failures and stop the servers from providing normal services. Zou et al. present Memshepherd, a system that can probabilistically prevent software from both stack and heap memory bugs and guarantee soundness of the software execution. A Linux prototype is implemented and tested against four kinds of memory bugs. The experiment results prove that Memshepherd is effective in eliminating crashes, erroneous execution, and security vulnerability.
Cloud data centers provide many resource provisioning mechanisms for efficiently sharing CPU, memory, and disk resources. Li, Ma, and Li address the issues of network performance and congestion in cloud data center networks, and present a fixed point model to attain optimal performance for a cloud network based on the theory of the supply-demand equilibrium. Numerical and simulation results show the effectiveness of proposed algorithm.