Extracting trust information from security system of a service

Bahtiyar, Şerif; Caglayan, M.U.

doi:10.1016/j.jnca.2011.10.002

Cited by 32 publications

(15 citation statements)

References 15 publications

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“…One of the negative feelings is not being able to trust. Trust is an individual's belief or an expectation of others' ethical behaviors under various influential factors, such as subjective norms, risk, confidence, and security (Mayer et al, 1995;Grandison and Sloman, 2000;Bahtiyar and Çağlayan, 2012). Researchers use these influential factors to determine the tendency of trust (McKnight et al, 2004;Riegelsberger et al, 2005) and construct trust as a central aspect in all types of relationships (Gefen et al, 2003).…”

Section: Trustmentioning

confidence: 99%

“…Spying on employees' privacy has significantly damaged the trust relationship. The implementation of spying technologies can be described as a manipulation or an outlook of a trusted computing base, which is viewed as "a bridge between social needs and security solutions to cope with trust management vulnerabilities for distributed networks" (Sun et al, 2008;Bahtiyar and Çağlayan, 2012). However, researchers highlight that justifying the system and policy to manipulate trust is not as necessary as experimenting in practice (Gollmann, 2006).…”

Section: Trustmentioning

confidence: 99%

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Exploring privacy and trust for employee monitoring

Chang

Liu

Lin

2015

Industrial Management &Amp; Data Systems

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Purpose – The purpose of this paper is to evaluate privacy boundaries and explores employees’ reactions in employee monitoring. Design/methodology/approach – The research used the metaphor of boundary turbulence in the Communication Privacy Management (CPM) theory to demonstrate the psychological effect on employees. The model comprised organizational culture, CPM, trust, and employee performance in employee monitoring to further investigated the influence exerted by organizational culture and how employees viewed their trust within the organization when implementing employee monitoring. Variables were measured empirically by administrating questionnaires to full-time employees in organizations that currently practice employee monitoring. Findings – The findings showed that a control-oriented organizational culture raised communication privacy turbulence in CPM. The communication privacy turbulence in CPM mostly had negative effects on trust in employee monitoring policy, but not on trust in employee monitoring members. Both trust in employee monitoring policy and trust in employee monitoring members had positive effects on employee commitment and compliance to employee monitoring. Research limitations/implications – This research applied the CPM theory in workplace privacy to explore the relationship between employees’ privacy and trust. The results provide insights of why employees feel psychological resistance when they are forced to accept the practice of employee monitoring. In addition, this study explored the relationship between CPM and trust, and offer support and verification to prior studies. Practical implications – For practitioners, the findings help organizations to improve the performance of their employees and to design a more effective environment for employee monitoring. Originality/value – A research model was proposed to study the impacts of CPM on employee monitoring, after a broad survey on related researches. The validated model and its corresponding study results can be referenced by organization managers and decision makers to make favorable tactics for achieving their goals of implementing employee monitoring.

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Section: Trustmentioning

confidence: 99%

Section: Trustmentioning

confidence: 99%

Exploring privacy and trust for employee monitoring

Chang

Liu

Lin

2015

Industrial Management &Amp; Data Systems

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“…Such features increase network service surfaces, leading to frequent security risks and attacks, such as hacking, interruption, acknowledgement spoofing, DoS, man-in-the-middle attack protocol tunnelling and interception [83]. Furthermore, the Internet network, which is a key component that connects IoT devices, has different players ranging from business subscribers to individual subscribers and from a local network area (LAN) to a worldwide network area (WAN), thereby connecting a wide range of devices and servers [84]. On the one hand, the Internet can provide a wide range of services and applications that can work in synergy with the information collected from sensors to achieve a fully functional IoT system for providing intelligent services.…”

Section: ) Network Service Surfacementioning

confidence: 99%

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

780

333

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The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT technologies play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT ecosystem effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. The ability to monitor IoT devices intelligently provides a significant solution to new or zero-day attacks. ML/DL are powerful methods of data exploration for learning about 'normal' and 'abnormal' behaviour according to how IoT components and devices perform within the IoT environment. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems.IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

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“…A large number of trust solutions appeared in various distributed networks, such as P2P, Ad hoc, WSN [2] [4][5][6][7].…”

Section: Some Elements Of Trust Managementmentioning

confidence: 99%

“…We consider a particular condition that the depth of the trust tree t=1, suppose factors F={f 1 ,f 2 ,…,f n } denote n leaves, evaluation set S={s 1 ,s 2 …”

Section: A Extracting Trust Information 1) Trust Based On Sensor Laymentioning

confidence: 99%

Distributed Trust Management Mechanism for the Internet of Things

Wang¹,

Sun²,

Yu³

et al. 2013

Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

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Abstract-Trust management provides a potential solution for the security issues of distributed networks. However, there are rare researches about the trust mechanism for IoT in the literature. A new distributed trust management mechanism for IoT is established in this paper. Firstly, we extract three basic elements-service, decision-making and self-organizing, of trust management from the investigated trust solutions. Then, based on a service model, we establish a trust management framework for the layered IoT, which is decomposed into three layers: sensor layer, core layer and application layer. Finally, we use fuzzy set theory and formal semantics-based language to perform the layered trust mechanism. The proposed trust conception, layered service model and formal method provide a general framework for the study of trust management for the IoT, and further provide a significant reference for the development of sound trust models for IoT.

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Extracting trust information from security system of a service

Cited by 32 publications

References 15 publications

Exploring privacy and trust for employee monitoring

Exploring privacy and trust for employee monitoring

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Distributed Trust Management Mechanism for the Internet of Things

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