Service Composition Recommendation Method Based on Recurrent Neural Network and Naive Bayes

Chen, Ming; Cheng, Jian; Ma, Guonan; Tian, Liang; Li, Xiaohong; Shi, Qingmin

doi:10.1155/2021/1013682

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Using the same dataset NSL-KDD, the detection accuracy of seven traditional machine learning algorithms [20][21][22][23][24][25][26][27][28][29][30][31][32][33] such as decision tree, Naive Bayes, Naive Bayes tree, random tree, random forest, support vector machine, multilayer perceptron, and the detection accuracy of the FCRNN-IDS model in the case of 2-class (normal, abnormal) and 5-class (normal, probe, Dod, R2L and U2R) were studied and compared.…”

Section: Comparative Experiments 1: Comparison With Traditional Machi...mentioning

confidence: 99%

An Intrusion Detection Method Based on Fully Connected Recurrent Neural Network

2022

Scientific Programming

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Now, the use of deep learning technology to solve the problems of the low multiclassification task detection accuracy and complex feature engineering existing in traditional intrusion detection technology has become a research hotspot. In all kinds of deep learning, recurrent neural networks (RNN) are very important. The RNN processes 41 feature attributes and maps them to a 122-dimensional high-dimensional feature space. To detect multiclassification tasks, this study proposes an intrusion detection method based on fully connected recurrent neural networks and compares its performance with previous machine learning methods on benchmark datasets. The research results show that the intrusion detection system (IDS) model based on fully connected recurrent neural network is very suitable for classification of intrusion detection. Classification methods, especially in multiclassification tasks, have high detection accuracy, significantly improve the detection performance of detection attacks and DoS attacks, and it provides a new research direction for the future attempts of intrusion detection methods for industrial control systems.

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Section: Comparative Experiments 1: Comparison With Traditional Machi...mentioning

confidence: 99%

An Intrusion Detection Method Based on Fully Connected Recurrent Neural Network

2022

Scientific Programming

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A Graph Neural Network Recommendation Method Integrating Multi Head Attention Mechanism and Improved Gated Recurrent Unit Algorithm

Liu,

Wang,

Yang

2023

IEEE Access

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To improve the accuracy of graph neural network recommendation algorithms, research mainly integrates multi head attention mechanism and GRU to construct a graph neural network recommendation model; Considering the long and short term preferences of users, a graph neural network algorithm integrating long and short term preferences is constructed. The research results indicated that when the embedding dimension was 64, the batch size of selected samples was 64, the learning rate was 0.0005, the vertical stacking layer of GRU was 2, the iteration period was 5, and the dropout probability was 50% with the best performance. The graph neural network algorithm based on long and short term preferences had higher recommendation accuracy compared to other algorithms. Modeling users' short-term and long-term preferences can achieve the goal of comprehensively improving recommendation effectiveness.

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Service Composition Recommendation Method Based on Recurrent Neural Network and Naive Bayes

Cited by 2 publications

References 23 publications

An Intrusion Detection Method Based on Fully Connected Recurrent Neural Network

An Intrusion Detection Method Based on Fully Connected Recurrent Neural Network

A Graph Neural Network Recommendation Method Integrating Multi Head Attention Mechanism and Improved Gated Recurrent Unit Algorithm

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