A Neural Network-Based Trust Management System for Edge Devices in Peer-to-Peer Networks

Alhussain, Alanoud; Kurdi, Heba; Altoaimy, Lina

doi:10.32604/cmc.2019.05848

Cited by 23 publications

(16 citation statements)

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“…BP neural network is a multilayer feedforward network trained by error propagation. e neural network consists of two parts: the forward transmission of information and the reverse transmission of errors [15][16][17]. A large number of neuron nodes are distributed in the neural network, and each neuron is connected by weights and thresholds to form a three-layer neural network structure.…”

Section: Neural Networkmentioning

confidence: 99%

[Retracted] Neural Network Optimal Routing Algorithm Based on Genetic Ant Colony in IPv6 Environment

Zou

et al. 2021

Computational Intelligence and Neuroscience

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The traditional IPv6 routing algorithm has problems such as network congestion, excessive energy consumption of nodes, and shortening the life cycle of the network. In response to this phenomenon, we proposed a routing optimization algorithm based on genetic ant colony in IPv6 environment. The algorithm analyzes and studies the genetic algorithm and the ant colony algorithm systematically. We use neural network to build the initial model and combine the constraints of QoS routing. We effectively integrate the genetic algorithm and ant colony algorithm that maximize their respective advantages and apply them to the IPv6 network. At the same time, in order to avoid the accumulation of a lot of pheromones by the ant colony algorithm in the later stage of the network, we have introduced an anticongestion reward and punishment mechanism. By comparing the search path with the optimal path, rewards and punishments are based on whether the network path is smooth or not. Finally, it is judged whether the result meets the condition, and the optimal solution obtained is passed to the BP neural network for training; otherwise, iterative iterations are required until the optimal solution is satisfied. The experimental results show that the algorithm can effectively adapt to the IPv6 routing requirements and can effectively solve the user’s needs for network service quality, network performance, and other aspects.

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Section: Neural Networkmentioning

confidence: 99%

[Retracted] Neural Network Optimal Routing Algorithm Based on Genetic Ant Colony in IPv6 Environment

Zou

et al. 2021

Computational Intelligence and Neuroscience

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“…(1) When a simple network structure is adopted, its convergence speed is very fast [21], and it can achieve a very good effect on the training set (the accuracy rate tends to be close to 1), but the performance effect on the test set is very poor (the accuracy rate is less than 0.5), that is, the phenomenon of overfitting.…”

Section: Tf-idf-based Detection Methodmentioning

confidence: 99%

Malicious Encryption Traffic Detection Based on NLP

Yang

Liu

et al. 2021

Security and Communication Networks

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The development of Internet and network applications has brought the development of encrypted communication technology. But on this basis, malicious traffic also uses encryption to avoid traditional security protection and detection. Traditional security protection and detection methods cannot accurately detect encrypted malicious traffic. In recent years, the rise of artificial intelligence allows us to use machine learning and deep learning methods to detect encrypted malicious traffic without decryption, and the detection results are very accurate. At present, the research on malicious encrypted traffic detection mainly focuses on the characteristics’ analysis of encrypted traffic and the selection of machine learning algorithms. In this paper, a method combining natural language processing and machine learning is proposed; that is, a detection method based on TF-IDF is proposed to build a detection model. In the process of data preprocessing, this method introduces the natural language processing method, namely, the TF-IDF model, to extract data information, obtain the importance of keywords, and then reconstruct the characteristics of data. The detection method based on the TF-IDF model does not need to analyze each field of the data set. Compared with the general machine learning data preprocessing method, that is, data encoding processing, the experimental results show that using natural language processing technology to preprocess data can effectively improve the accuracy of detection. Gradient boosting classifier, random forest classifier, AdaBoost classifier, and the ensemble model based on these three classifiers are, respectively, used in the construction of the later models. At the same time, CNN neural network in deep learning is also used for training, and CNN can effectively extract data information. Under the condition that the input data of the classifier and neural network are consistent, through the comparison and analysis of various methods, the accuracy of the one-dimensional convolutional network based on CNN is slightly higher than that of the classifier based on machine learning.

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“…Our method integrates the advantages of Regression-based and end-to-end methods. We propose CTSF, which can be generalized as a mix of CTPN, PVANet [9][10][11], Spatial Pyramid Pooling (SPP) and skeleton extraction algorithm. PVANet is used as an efficient backbone network to extract robust features.…”

Section: Related Workmentioning

confidence: 99%

CTSF: An End-to-End Efficient Neural Network for Chinese Text with Skeleton Feature

Wang¹,

Jin²,

Ren³

2021

Journal on Big Data

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A Neural Network-Based Trust Management System for Edge Devices in Peer-to-Peer Networks

Cited by 23 publications

References 0 publications

[Retracted] Neural Network Optimal Routing Algorithm Based on Genetic Ant Colony in IPv6 Environment

[Retracted] Neural Network Optimal Routing Algorithm Based on Genetic Ant Colony in IPv6 Environment

Malicious Encryption Traffic Detection Based on NLP

CTSF: An End-to-End Efficient Neural Network for Chinese Text with Skeleton Feature

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