TMSRS: trust management-based secure routing scheme in industrial wireless sensor network with fog computing

Fang, Weidong; Zhang, Wuxiong; Chen, Wei; Liu, Yang; Tang, Chaogang

doi:10.1007/s11276-019-02129-w

Cited by 77 publications

(43 citation statements)

References 32 publications

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“…Based on historical behavior, evaluating the trust value of a node can estimate its trustworthiness to perform a particular task. Currently, the research on trust decision based on trust value mainly includes two aspects: one is the detection of malicious nodes and the defense against internal attacks, especially, defending against the On‐Off attack, and the other is to select the next hop node and to improve the security of routing protocol 10 . In this section, we will discuss and analyze the above‐mentioned two aspects.…”

Section: Related Workmentioning

confidence: 99%

BTDS: Bayesian‐based trust decision scheme for intelligent connected vehicles in VANETs

Fang

Zhang

Liu

et al. 2020

Trans Emerging Tel Tech

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Intelligent connected vehicles (ICVs) are becoming the mainstream of the global automotive industry in the future, and its information security issues gradually emerge. The heterogeneity of Vehicle Ad hoc NETworks (VANETs) and the mobility of terminal nodes make the defense against the security attacks very complex, especially the internal attacks. In this article, toward defense against the internal attacks for ICVs in VANETs, we first propose a Gaussian-distribution-based and third-party-recommendation trust management model, which is called GTTMM, to solve the issue that the trust value increases too fast under continuous cooperation. And Bayesian-based trust decision scheme extending from GTTMM is named as BDTS, which incorporates the Bayesian Network, for defending against a variant of On-Off attack, called high trust value On-Off attack (HTV-On-Off attack). With the simulation study, it is demonstrated that, compared with reputation-based framework for high integrity sensor networks (RFSN), a traditional trust management model under the Beta distribution, GTTMM can better satisfy the principle of trust value, that is, "hard to get, easy to lose", while BDTS can defend against the HTV-On-Off attack effectively.

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Section: Related Workmentioning

confidence: 99%

BTDS: Bayesian‐based trust decision scheme for intelligent connected vehicles in VANETs

Fang

Zhang

Liu

et al. 2020

Trans Emerging Tel Tech

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“…A Gaussian distribution‐based comprehensive trust management system was introduced in Reference 24. But, attack detection was not effectively performed in Reference 25 a fountain‐coding aided strategy utilized to ensure secure transmission based on cooperative model in industrial wireless sensor networks.…”

Section: Related Workmentioning

confidence: 99%

Bipolar fully recurrent deep structured neural learning based attack detection for securing industrial sensor networks

Alzubi

2020

Trans Emerging Tel Tech

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Attack detection is a significant problem to be resolved to attain security in industrial sensor network. Few research works have been designed for performing attack discovery process with the help of different classification algorithms. However, classification performance was lowered to accurately find attack nodes and thereby getting higher security level (SL). In order to overcome such limitations, bipolar fully recurrent deep structured neural learning based attack detection (BFRDSNL‐AD) technique is proposed in this article. The BFRDSNL‐AD technique comprises of three main layers, namely, input, hidden, and output layer to accurately carry‐out attack detection process. The input layer in BFRDSNL‐AD Technique takes a number of sensor nodes as input. The designed BFRDSNL‐AD technique utilized numbers of hidden layers in order to deeply examine each sensor node in industrial sensor network. The result of the hidden layer is fed‐back into the network along with the inputs in order to discover the temporal dynamic behavior of sensor nodes. In BFRDSNL‐AD technique, output layer applies bipolar activation function to determine the association between identified node features to give attack detection result. If the output layer result is 1, then sensor node is considered as normal. Otherwise, sensor node is considered as malicious attack in industrial sensor network. Thus, the BFRDSNL‐AD technique increases the attack detection performance with higher accuracy and minimal time. From that, BFRDSNL‐AD technique gets enhanced SL in industrial sensor network. The simulation of BFRDSNL‐AD technique is conducted using metrics such as security, data delivery rate, data loss rate, and delay with respect to a different number of sensor nodes and data packets.

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“…In [15], researchers have proposed a dynamic trust framework to detect selfish and malicious nodes, and the entire network utility has been improved. Not only for the network, but the routing protocols also improve their security due to the introduction of trust [21]. Table 2.…”

Section: Trust Model Trust Mechanism and Trust Framework In Networkmentioning

confidence: 99%

“…These attacks can disrupt the transmission link of the network, which in turn disrupts network availability. How to choose a reliable node for transmission, and how to isolate malicious nodes out from the network quickly and accurately, are crucial [21][22][23]. To find the optimal choice in distributed network, game theory is an effective solution [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Towards Security Joint Trust and Game Theory for Maximizing Utility: Challenges and Countermeasures

Huang

Jia

Fang

et al. 2019

Sensors

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The widespread application of networks is providing a better platform for the development of society and technology. With the expansion of the scope of network applications, many issues need to be solved. Among them, the maximization of utility and the improvement of security have attracted much attention. Many existing attacks mean the network faces security challenges. The concept of trust should be considered to address these security issues. Meanwhile, the utility of the network, including efficiency, profit, welfare, etc., are concerns that should be maximized. Over the past decade, the concepts of game and trust have been introduced to various types of networks. However, there is a lack of research effort on several key points in distributed networks, which are critical to the information transmission of distributed networks, such as expelling malicious nodes quickly and accurately and finding equilibrium between energy assumption and high transmission rate. The purpose of this paper is to give a holistic overview of existing research on trust and game theory in networks. We analyzed that network utility can be maximized in terms of effectiveness, profits, and security. Moreover, a possible research agenda is proposed to promote the application and development of game theory and trust for improving security and maximizing utility.

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TMSRS: trust management-based secure routing scheme in industrial wireless sensor network with fog computing

Cited by 77 publications

References 32 publications

BTDS: Bayesian‐based trust decision scheme for intelligent connected vehicles in VANETs

BTDS: Bayesian‐based trust decision scheme for intelligent connected vehicles in VANETs

Bipolar fully recurrent deep structured neural learning based attack detection for securing industrial sensor networks

Towards Security Joint Trust and Game Theory for Maximizing Utility: Challenges and Countermeasures

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