Improving security and stability of ad hoc on-demand distance vector with fuzzy neural network in vehicular ad hoc network

Mo, Jiawei; Huang, Baohua; Huang, Chen; Feng, Wei

doi:10.1177/1550147718806193

Cited by 7 publications

(5 citation statements)

References 13 publications

(18 reference statements)

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“…The fuzzy trust model relies on a series of fuzzy rules to deal with the uncertainty in trust evaluation. Then, fuzzy theory can be used to evaluate the trust value and select the trusted route [31][32][33]. (d) Directed and undirected graph model.…”

Section: Intrusion Detection Intrusion Detection Is Used To Detect Amentioning

confidence: 99%

Information-Aware Secure Routing in Wireless Sensor Networks

Shi

Ding

et al. 2019

Sensors

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Secure routing is crucial for wireless sensor networks (WSNs) because they are vulnerable to various attacks. In this paper, we propose a new secure routing protocol for WSNs in the presence of malicious nodes. For each relay node in the route, associated information such as its trust value and status is considered in the protocol. The trust value is defined as the attack probability of the node according to previous packet-forwarding behaviors, and the status is a hybrid metric that combines the residual energy and distance to the sink node. Therefore, the route generated by the protocol is secure against malicious attacks and globally optimal according to the associated information. We used an improved variant of the Dijkstra algorithm to generate the secure route for WSNs in the presence of malicious nodes. Compared with the Reputation-Based Mechanism to Stimulate Cooperation (RBMSC) model in the same simulation environment, the proposed model can maintain a higher delivery ratio, which verifies the effectiveness of the proposed model on the basis of global optimization. Furthermore, compared with the traditional Dijkstra algorithm, the packet loss ratio in the improved Dijkstra algorithm is lower because it can more effectively avoid malicious nodes, thus verifying the effectiveness of the improved algorithm.

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Section: Intrusion Detection Intrusion Detection Is Used To Detect Amentioning

confidence: 99%

Information-Aware Secure Routing in Wireless Sensor Networks

Shi

Ding

et al. 2019

Sensors

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“…In this topic, we can break the ML application in VANETs further into MAC layer [29] and Network Layer [30][31][32][33][34][35][36].…”

Section: A ML For Qos Guaranteementioning

confidence: 99%

“…The mechanism of update Q-Table reward node balance the next-hop selection between direction connection with BS or neighboring gateway with large number of devices. [36] propose an improved AODV routing protocol based on fuzzy neural network. This neural network is used to calculate the node stability.…”

Section: A ML For Qos Guaranteementioning

confidence: 99%

Intelligent Processing in Vehicular Ad hoc Networks: a Survey

Liu

2019

Preprint

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The intelligent Processing technique is more and more attractive to researchers due to its ability to deal with key problems in Vehicular Ad hoc networks. However, several problems in applying intelligent processing technilogies in VANETs remain open. The existing applications are comprehensively reviewed and discussed, and classified into different categories in this paper. Their strategies, advantages/disadvantages, and performances are elaborated. By generalizing different tactics in various applications related to different scenarios of VANETs and evaluating their performances, several promising directions for future research have been suggested.

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“…So far, it has become the mainstream to use entropy correlation fuzzy evaluation system to fuzzy the subjective evaluation results of experts, and then use machine learning model to classify the fuzzy features to obtain the network security evaluation results. The existing entropy‐related fuzzy evaluation system uses the subjective evaluation scores of experts to calculate the safety membership matrix, and then constructs the corresponding fuzzy evaluation weight matrix by the way of membership, and then puts the weight matrix into the machine learning model for regression, and predicts the network safety results of the current system 10 . In fact, the subjective bias of experts will accumulate continuously in a period of time, which will eventually have a great impact on the accuracy and robustness of network security situation prediction results.…”

Section: Introductionmentioning

confidence: 99%

“…The existing entropy-related fuzzy evaluation system uses the subjective evaluation scores of experts to calculate the safety membership matrix, and then constructs the corresponding fuzzy evaluation weight matrix by the way of membership, and then puts the weight matrix into the machine learning model for regression, and predicts the network safety results of the current system. 10 In fact, the subjective bias of experts will accumulate continuously in a period of time, which will eventually have a great impact on the accuracy and robustness of network security situation prediction results. In order to solve the problem that the existing machine learning model cannot deal with the relationship between the data before and after the time sequence, resulting in the accumulation of subjective deviation which has a greater impact on the evaluation of network security, this paper proposes a network security state prediction algorithm combining D-S evidence theory and cyclic neural network.…”

Section: Introductionmentioning

confidence: 99%

Network security situation prediction based on combining associated entropy and deep recurrent neural network

Xin-cun

Wang

2020

Trans Emerging Tel Tech

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The existing network security situation prediction algorithm cannot deal with the subjective attitude deviation of multi-experts, and the traditional sequential machine learning model cannot deal with the problem of deviation accumulation in time period. In this paper, an algorithm of network security situation prediction combining entropy correlation and deep time series network model is proposed. First, the expert fuzzy evaluation index is constructed by trigonometric fuzzy function, and the modified weighted dempster-shafer (DS) evidence reasoning correction index is used, then the loss and possibility matrix features are created, and finally the information security is evaluated by deep time series network. The simulation experiments are carried out on MIT dataset. The experiments analyze whether the features can cope with multi-expert conflicts, and evaluate the accuracy, robustness, and time efficiency of the algorithm.The experimental results show that the algorithm proposed in this paper has stronger fuzzy evaluation ability, stronger ability to deal with conflict opinions among experts, more accurate prediction of network security situation in time sequence, and higher robustness, but the efficiency of the algorithm has been maintained.

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Improving security and stability of ad hoc on-demand distance vector with fuzzy neural network in vehicular ad hoc network

Cited by 7 publications

References 13 publications

Information-Aware Secure Routing in Wireless Sensor Networks

Information-Aware Secure Routing in Wireless Sensor Networks

Intelligent Processing in Vehicular Ad hoc Networks: a Survey

Network security situation prediction based on combining associated entropy and deep recurrent neural network

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