A Research of Network Applications Classification Based on Deep Learning

Shao, Hong; Tang, Liujun; Dong, Ligang; Chen, Long; Jiang, Xian; Wang, Weiming

doi:10.1007/978-3-030-00557-3_2

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Traffic Classification Based On Deep Belief Network1

Introduction1

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2020

2023

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Cited by 7 publications

(2 citation statements)

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“…In 2018, Hong Shao et al [52] proposes proposes a network application classification model based on Deep Belief Networks and construct a DBN-based model suitable for network applications classification with the Tensorflow framework. the classification performances of this DBN-based model and the BP-based model are compared on the real data sets.…”

Section: Traffic Classification Based On Deep Belief Networkmentioning

confidence: 99%

Network Traffic Classification Based on Deep Learning

2020

KSII TIIS

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Section: Traffic Classification Based On Deep Belief Networkmentioning

confidence: 99%

Network Traffic Classification Based on Deep Learning

2020

KSII TIIS

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“…Deep Belief Networks,also called graphical generative models address the automatic learning of features and also learn the important features that characterize a given dataset and get over the training's challenges by using the layer by ayer initialization technique. The application of the Deep Belief Neural network algorithm for characteristic recognition and categorization offers obviuos benefits [13]. The rest of this study is organized as follows: Section 2 discusses the prior research on android based applications cateorization using machine learning and deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

Android applications classification with deep neural networks

et al. 2023

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Article Title our neural network's classification performance against that of four traditional deep feed-forward neural networks and seven baseline models based on machine learning algorithms on the same data. According to experimental results, a deep belief neural network-based model could accurately categorize Android apps into benign and malicious categories with 98.7% of the time. Compared to all previous deep learning and machine learning methods, this represents a significant improvement. Also,the categorization accuracy of the DBN model is better than that of numerous other models examined by earlier researchers.

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