A green trust‐distortion resistant trust management scheme on mobile ad hoc networks

Movahedi, Zeinab; Hosseini, Zahra

doi:10.1002/dac.3331

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…The scheme can detect and deal with malicious attacks, and the reliability evaluation of data and mobile nodes is also implemented. Movahedi et al 29 proposed a green trust management scheme, which can deal with different trust distortion attacks in multiple attack environments. According to the security problems in different scenarios of IoT, these schemes put forward corresponding trust management schemes and achieved good results.…”

Section: Related Workmentioning

confidence: 99%

A decentralized and reliable trust measurement for edge computing enabled Internet of Things

Zhang

Cao

Ning

2022

Concurrency and Computation

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The combination of edge computing (EC) and the Internet of Things is a hot research topic. And security is one of the most important problems to be solved. The trust measurement of devices is an effective way to solve the security problem, and the lack of a unified trust measurement model makes the untrusted devices destroy the quality of service. Establishing a reliable trust relationship between devices can effectively improve the security of the system. A decentralized trust measurement model for devices is proposed. First, a decentralized trust measurement framework is proposed, which combines EC with blockchain technology to establish a decentralized hierarchical structure; second, the credibility of devices is measured from multilevel and multi-attribute; finally, a feedback trust filtering mechanism is designed to filter reliable feedback information, and then the trust between devices and the comprehensive trust of devices are calculated. Experiments and analysis show that the proposed decentralized trust measurement model can effectively measure the trust degree of devices and resist various malicious feedback attacks. K E Y W O R D Sblockchain, edge computing, Internet of Things, trust measurement INTRODUCTIONWith the development of Internet of Things (IoT) technology, IoT has been widely used in all walks of life, such as smart city, 1 smart home, 2 intelligent logistics, 3 environmental monitoring 4 and so on. At the same time, the requirements for IoT and its related infrastructure are also improved, including system security, ultra-low latency, low power consumption, and data reliability. 5 In addition, the combination of artificial intelligence [6][7][8] intelligent optimization algorithm 9-11 and other technologies with the IoT is more and more, which further increases the demand of terminal equipment for computing, storage and other performance. The traditional cloud computing paradigm 12,13 is far from the edge of the network and the resources are relatively concentrated. The existing cloud based IoT model is difficult to meet the above new application requirements. The scheduling mode 14,15 based on cloud computing is also difficult to be used for a large number of IoT devices. The development of mobile edge computing (MEC) technology provides a reliable solution for these application requirements. 16,17 MEC technology is to deploy the basic implementation of mobile cloud computing to the edge of the network, to process data at the edge of the network, reduce the response time of service requests, reduce the energy consumption of mobile devices, reduce the network bandwidth, and provide a new idea for data security and network security.At present, the edge computing enabled IoT is a research hotspot in the academic and industrial circles, which has attracted extensive attention from scholars.The devices of the IoT sensing layer are usually deployed in complex scenes, such as sensors and cameras for monitoring purposes. Limited by the ability of storage, computing, energy consumption and other a...

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Section: Related Workmentioning

confidence: 99%

A decentralized and reliable trust measurement for edge computing enabled Internet of Things

Zhang

Cao

Ning

2022

Concurrency and Computation

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“…It also checks the transmission failure and provides high fault tolerance. GTRTMS 84 method has the capacity of self‐adaptation and detection of trust‐distortion attacks. This method calculates the trust by overhearing the packets in the promiscuous mode.…”

Section: Classification and Reviewmentioning

confidence: 99%

Capitulation of mitigation techniques of packet drop attacks in MANET to foreground nuances and ascertain trends

Singh¹,

Bhasin

Kalia³

2021

Int J Communication

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MANET is a kind of wireless ad hoc networks, in which mobile nodes operate in dynamic, distributed, and cooperative environment. Open nature of MANET makes it vulnerable to various kinds of attacks. Of these, the most severe are the packet drop attacks such as Blackhole attack, Cooperative Blackhole, and Grayhole attacks. The malicious nodes drop the packets as these packets are routed through them in accordance with the malefic strategy of the particular packet drop attack, which adversely affects the network performance. A substantial amount of research papers focus on dealing with packet drop attacks in the last few years, but security is still an impending issue. In this paper, we have presented the systematic review of the literature of last 6 years on all variants of packet drop attacks. We have classified these techniques into four categories, namely, mathematical based, monitoring based, network dependent, and amalgamation based which are further sub divided into 12 categories. These papers are categorized in a well-defined structure to bring out their contribution, to underline the left-out things, and to foreground the emerging issues for future research. Detailed observations are recorded following a panoptic study in order to identify and divulge more eligible and effective mitigation techniques in a particular environment.

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“…In recent years, trust models have been the subject of study in various networking contexts other than IoT, including wireless sensor networks (WSNs), mobile ad hoc networks and peer‐to‐peer networks, just to mention a few. However, these works cannot be directly applied for the D2D context due to the resource constraints of IoT devices and their different objective matter (service offloading in D2D versus routing in most of other contexts).…”

Section: Related Workmentioning

confidence: 99%

T‐D2D: A trust model for service offloading in device‐to‐device communication

Movahedi

Hosseini

2019

Trans Emerging Tel Tech

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With the advances in pervasive computing, Internet of things (IoT) has gained considerable attention from both research and industrial communities. While IoT devices are able to provide computational services to other devices via device‐to‐device (D2D) communications, they are not guaranteed to be honest and collaborative. In such a context, the trust model can help to detect malicious service providers. However, malicious nodes may perform trust‐distortion attacks to mislead the trust model. They may perform on‐off attacks to remain undetected or bad‐mouth about others to make it difficult to infer if a contradictory recommendation comes from the on‐off nature of the evaluated node or dishonesty of the recommender. To address these issues, we propose T‐D2D, a lightweight trust model capable to face simultaneous trust‐distortion attacks. T‐D2D evaluates a node's nature using both short‐term and long‐term evaluation intervals to detect different types of on‐off attacks. Moreover, it keeps track of marginal misbehaving over several successive intervals to recognize the nature of suspicious on‐off nodes with light misbehaving attitude. To face bad‐mouthing attackers, T‐D2D limits its dependence on recommendations to when the direct trust is not decisive. Moreover, it evaluates the honesty of a recommender based on the correctness of its recommendations over time. Simulation results prove that T‐D2D exhibits significantly better performance than other counterparts in terms of trust level, correctness of calculated trust, percentage of selected malicious providers, total wasted execution time, and energy consumption in presence of simultaneous trust‐distortion attacks.

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A green trust‐distortion resistant trust management scheme on mobile ad hoc networks

Cited by 6 publications

References 14 publications

A decentralized and reliable trust measurement for edge computing enabled Internet of Things

A decentralized and reliable trust measurement for edge computing enabled Internet of Things

Capitulation of mitigation techniques of packet drop attacks in MANET to foreground nuances and ascertain trends

T‐D2D: A trust model for service offloading in device‐to‐device communication

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