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A Survey of Intrusion Detection Systems in Wireless Sensor Networks
Published in Georgios Kambourakis, Asaf Shabtai, Constantinos Kolias, Dimitrios Damopoulos, Intrusion Detection and Prevention for Mobile Ecosystems, 2017
Eleni Darra, Sokratis K. Katsikas
In Reference 42, a hierarchical trust management protocol that leverages clustering to cope with a large number of heterogeneous SNs for scalability and reconfigurability, as well as to cope with selfish or malicious SNs for survivability and intrusion tolerance with vastly different social and QoS behaviors, is described. The authors address the key design issues of trust management, including trust composition, trust aggregation, and trust formation. The hierarchical trust management protocol is resilient to blackhole, sinkhole, and slandering (RepTrap) attacks. The trust-based IDS algorithm outperforms traditional anomaly-based IDS techniques in terms of detection rate, while maintaining sufficiently low false positives. The strength of the trust-based IDS algorithm is especially pronounced when FPR approaches zero: the trust-based IDS algorithm can still maintain a high detection rate (>90%) when FPR is close to zero, a point at which the detection rate of anomaly detection-based IDS schemes drops sharply.
Trust Management in IoT
Published in Ricardo Armentano, Robin Singh Bhadoria, Parag Chatterjee, Ganesh Chandra Deka, The Internet of Things, 2017
Avani Sharma, Pilli Emmanuel Shubhakar, Arka Prokash Mazumdar
Trust management comprises of managing and building the reputation of entities locally (direct measure) and globally (indirect measure) in the system. In open systems similar to IoT in which devices can join and leave the network at any moment of time, maintaining trust and reputation between heterogeneous devices is a challenging task. Even after embedding trust management strategies, the system can be compromised by insider adversaries. These adversaries can launch the attack either for self-benefit or to perform malicious activity (Koutrouli and Tsalgatidou, 2012). A selfish attacker subverts the system performance with the intension of increasing own benefit, whereas a malicious attacker targets other devices to degrade the system performance. These attackers can work individually or may collude with other devices in the system (Marmol and Perez, 2009). In later category, attacking device forms a group with the other devices to perform a malicious activity and boost the effect of attack on the system performance. One major aspect that affects the process of trust management is IM. Identity belonging to an entity (device) plays an important role while building the reputation of that entity. These identities can be any real-world identity or can be defined by the users. Pseudonym in assigning identities allows attackers to bear multiple identities. If a system is compromised with faked identities, any trust management strategy can be vulnerable toward attacks. Therefore, a system is assumed to embed efficient IM scheme for managing authentication between heterogeneous identities. We classify the attacks (Figure 10.5) into two categories where an attacker can disrupt either reputation management or IM system.
IoT: An Overview
Published in Chintan Patel, Nishant Doshi, Internet of Things Security, 2018
Trust management is defined as a approach to making decisions about interacting with something or someone we do not know, establishing whether we should proceed with the interaction or not. Trust management can differentiate in two types. The first one is deterministic trust in which some policy or certification is defined for the trust establishment, while non-deterministic trust management depends on some dynamic parameters like recommendation, repudiation, prediction and social network. Policy based trust management will define a policy to find the minimum trust level of nodes.
Formulating Knowledge-Based Cloud Identity Selection
Published in Journal of Computer Information Systems, 2021
Brian Cusack, Eghbal Ghazizadeh
Trust management is a prominent area of security in the Cloud Computing (CC) environment because insufficient trust management hinders cloud growth.1,2 Moreover, trust management is one of the key concerns in the adoption of CC. Trust management systems research can help develop innovative solutions to challenges by strengthening protection for identification, privacy, personalization, integration, security, and scalability. In addition, the end user of services is better informed when making the best decision regarding the security,3 privacy,4 and Quality of Service (QoS).5,6 The cloud services attributes encountered by the user abstract into preferences and behaviors that decide usage and a preferred Cloud Service Provider (CSP). Consequently, Cloud Identity Users (CIdU) require evidence to assess the dependability of a CSP and Cloud Identity Providers (CIdP). Moreover, CSPs and CIdPs have to be able to factually, transparently, and objectively present the attributes and characteristics of their capabilities.7 Therefore, by achieving evidence for trust,8 CIdUs and Cloud Service Customers (CSC) have a basis to make good decisions about whether or not to depend on a particular CSP and CIdP out of many providers.9
An Empirical Study of Home IoT Services in South Korea: The Moderating Effect of the Usage Experience
Published in International Journal of Human–Computer Interaction, 2019
Trust management plays an important role in the use of IoT for reliable data fusion and mining, qualified services with context-awareness, and enhanced user privacy and information security (Accenture, 2015a). Trust becomes even more important when users share their personal information with their service providers. Home IoT service providers can collect and store personal information in the real world, and they can access to detailed behaviors of the user. Accenture (2015b) identified the four keys to trust — security, privacy, benefit/value, and accountability — and stated that the four dimensions of trust help establish a user’s trust in a service provider. As IoT matures, the success of service providers will depend on the level of trust that users have in them. Service providers set up the gateway that aggregates different services and applications, manage the quality of service of the home network, and carry out professional installations (Videonet, 2016). As users are willing to make risk-benefit decisions if there is some perceived value, home IoT service providers must be accountable for any lapse in protecting user information.
Evaluation of project completion time prediction accuracy in a disrupted blockchain-enabled project-based supply chain
Published in International Journal of Systems Science: Operations & Logistics, 2023
Furthermore, BCT combined with IoT sensors offers reliable authentication and trust level management (Moinet et al., 2017). Trust management can be conceptualised in two ways: the process by which an individual becomes trustworthy and assesses other individuals' reliability. The use of smart contracts can further improve supply chain efficiency and effectiveness. Smart contracts address the trust to establish contractual relationships, quickly and cheaply, among parties that may not know each other (Bottoni et al., 2020; Helo & Shamsuzzoha, 2020).