Classification Method of Encrypted Traffic Based on Deep Neural Network

Wan, Jing; Wu, Libing; Xia, Youhua; Hu, Jianzong; Xia, Zhenchang; Zhang, Rui; Wang, Min

doi:10.1007/978-981-15-0121-0_41

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…In this section, the performance of detection accuracy is compared with existing techniques such as DNS (Yan, Li, et al, (2020)), DL (Wan, Wu, et al, (2019)), and AICM (Cheng, Zhang, et al, (2019)). Thus it pronounced our proposed system for detection the APT in cloud performs in a great effective and accurate manner.…”

Section: Comparison Resultsmentioning

confidence: 99%

Threat-Path Estimate-Based Watchword-Chunk Algorithm for Advanced Persistent Threat in the Cloud

Pandipati

2022

International Journal of Web Services Research

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In cloud computing, an advanced persistent threat (APT) is a cyber-attack that gains access to a network and remains undetected for some time. As well APTs have proven difficult to detect and protect, in the existing system they fail to analyze the path of an outbreak when the monitor and assign a weight to the nodes. If a path for an outbreak is detected the VM is migrated to hosts that do not account for the overloaded problem and underutilized hosts. In addition to the size of resources occupied by the VM thus here the traffic was increased. This paper proposes the Threat-Path Reckon technique that detects the multiple paths through re-identification and the addition of automatic weight for its neighbor nodes. Based on that weighted paths, the Secured Object Emigration technique invokes a mapping function to migrate the VMs. Finally, the data in the VM are stored in a best-fit distribution, thus it provides security but achieves the search overheads.

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Section: Comparison Resultsmentioning

confidence: 99%

Threat-Path Estimate-Based Watchword-Chunk Algorithm for Advanced Persistent Threat in the Cloud

Pandipati

2022

International Journal of Web Services Research

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“…Since the structure studied in this paper is not pooled in the propagation mentioned above, the residual of the pooled layer can only be transferred to the convolutional layer. In the study of parameter update, in order to accurately obtain the above residuals and clarify the partial derivative of the loss function with respect to the weight value W and the offset b, the following formula should be used for calculation and analysis [12,13]:…”

Section: Ffi -Cnn Modelmentioning

confidence: 99%

Deep Learning-Based Encrypted Network Traffic Classification and Resource Allocation in SDN

Zhang

Yang

2021

JWE

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In the rapid development of network technology, with the improvement of the quality and quantity of network users’ demands, more and more network information technology and excessive network traffic also raise people’s attention to the internal network security. Especially for the classification and resource allocation of encrypted network traffic, the research of related technologies has become the main research direction of the development of network technology. The extensive application of deep learning provides a new idea for the study of traffic classification. Therefore, on the basis of understanding the current situation, the improved convolutional neural network is selected to conduct an in-depth discussion on traffic classification and resource allocation of encrypted networks based on deep learning. The performance of the system is verified from the perspective of practical application.

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A Distribution Network Automation Communication Module Based on 800MHz Wireless Communication Technology

Wenjin,

Zeyue,

Yino

et al. 2024

Lecture Notes in Electrical Engineering

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Classification Method of Encrypted Traffic Based on Deep Neural Network

Cited by 6 publications

References 19 publications

Threat-Path Estimate-Based Watchword-Chunk Algorithm for Advanced Persistent Threat in the Cloud

Threat-Path Estimate-Based Watchword-Chunk Algorithm for Advanced Persistent Threat in the Cloud

Deep Learning-Based Encrypted Network Traffic Classification and Resource Allocation in SDN

A Distribution Network Automation Communication Module Based on 800MHz Wireless Communication Technology

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