Encrypted network traffic classification based on machine learning

El-Maghraby, Reham Taher; Aziem, Nada M. Abdel; Sobh, Mohamed A.; Bahaa-Eldin, Ayman M.

doi:10.1016/j.asej.2023.102361

Cited by 8 publications

(1 citation statement)

References 8 publications

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“…It is becoming increasingly difficult to allocate cloud computing resources, and this is affecting a large proportion of organizations. Small and medium enterprises (SMEs) are most likely to be affected by these challenges [18][19].…”

Section: Introductionmentioning

confidence: 99%

Data Analytics Techniques for Addressing Cloud Computing Resources Allocation Challenges: A Bibliometric Analysis Approach

Sekwatlakwatla,

Malele

2024

J. Inf. Syst. Informatics

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The increase in the use of digital technology led to an increase in online activities. In this regard, many organizations adopted cloud computing systems to manage this online traffic. It is plan of every cloud computing resource provider to manage their system effectively and efficiently. This paper uses bibliometric analysis technique to look at the prevalence of utilization of data analytics techniques in addressing cloud computing resource allocation challenges. In this regard, the following research databases the Association for Computing Machinery, the Institute of Electrical and Electronics Engineering, Web of Science and Scopus databases, were consulted. The research articles published before the beginning of 2017 to 2023 were considered as part of the analysis. The results showed that the prevalent data analytics techniques used to address the cloud computing resources allocation challenge are Support Vector Machine, Spatio-temporal and edge-cloud collaborative scheme. Failure to effectively and efficiently provide cloud computing management resource allocation will lead to system bottlenecks especially during peak periods. In this regard, such a failure could lead to dissatisfied clients.

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Section: Introductionmentioning

confidence: 99%

Data Analytics Techniques for Addressing Cloud Computing Resources Allocation Challenges: A Bibliometric Analysis Approach

Sekwatlakwatla,

Malele

2024

J. Inf. Syst. Informatics

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Network traffic classification based‐ masked language regression model using CNN

P. L.,

Emmanuel,

Rani

2024

Concurrency and Computation

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SummaryNetwork traffic classification task has become increasingly challenging. The objective behind this classification is to effectively handle bandwidth, prioritize certain types of traffic, enhance application performance, and more. In recent times, there has been a surge in exploring deep learning approaches for network traffic categorization. However, these models demand substantial volumes of training data. Additionally, many classification methods necessitate manual feature extraction, a process that is not only time‐consuming but also laborious. Addressing the challenge of identifying optimal features to enhance classification accuracy, this work introduces a deep learning model designed for effective classification of network traffic. The model comprises the following key stages: (a) The dataset involves TCP flows captured from running different network stress and web crawling tools, (b) Pre‐processing for removal of anomalies and noises using Label Encoder and OneHotEncoder, (c) The utilization of K‐BERT for feature extraction aims to retrieve local spatial–temporal features, (d) feature selection using linear regression model (LASSO) and finally, and (e) The classification of network traffic involves neural network. The model serves to enhance the precision and efficiency of the classification mission. Through comprehensive experimental analysis, it was observed that the Masked Language‐based Regression model surpassed other referenced models, achieving an exceptional accuracy of 0.97.

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A Cost-Sensitive Sparse Auto-encoder Based Feature Extraction for Network Traffic Classification Using CNN

Steffi,

Sam Emmanuel,

Rani

2024

Information Systems Engineering and Management

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Encrypted network traffic classification based on machine learning

Cited by 8 publications

References 8 publications

Data Analytics Techniques for Addressing Cloud Computing Resources Allocation Challenges: A Bibliometric Analysis Approach

Data Analytics Techniques for Addressing Cloud Computing Resources Allocation Challenges: A Bibliometric Analysis Approach

Network traffic classification based‐ masked language regression model using CNN

A Cost-Sensitive Sparse Auto-encoder Based Feature Extraction for Network Traffic Classification Using CNN

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