Encryption-agnostic classifiers of traffic originators and their application to anomaly detection

Canavese, Daniele; Regano, Leonardo; Basile, Cataldo; Ciravegna, Gabriele; Lioy, Antonio

doi:10.1016/j.compeleceng.2021.107621

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…The traffic used to generate the dataset has been captured using WireShark 2.6.4 with the tshark command-line interface. The web browsing part of the traffic dataset has been generated by manually browsing the Internet, as reported in Section 4.1 of the main paper [1] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…Multiple truncated versions of the filtered captures have been generated. As explained in Section 4.1 of the main paper [1] , by truncating the captures, it has been possible to benchmark our models on incomplete connections, to test their suitability for a live analysis scenario. For instance, Fig.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…Finally, a dataset has been generated to train a set of machine learning models able to classify a TCP connection based on the tool category, the specific tool, and the specific version of the tool used to generate it. Section 5.1 of the main paper [1] describes in detail how the models have been trained. This document reports the hyperparameters used to train each of the models.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“… Since several of the trained Machine Learning models are based on neural networks, these data also may be used to speed up the training of new neural networks via transfer learning. These data allow assessing the results of research presented in [1] , which first aimed at obtaining information about the tools that originated sniffed network traffic. …”

Section: Value Of the Datamentioning

confidence: 99%

“…These data allow assessing the results of research presented in [1] , which first aimed at obtaining information about the tools that originated sniffed network traffic.…”

Section: Value Of the Datamentioning

confidence: 99%

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Data set and machine learning models for the classification of network traffic originators

et al. 2022

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Section: Value Of the Datamentioning

confidence: 99%

“…These data allow assessing the results of research presented in [1] , which first aimed at obtaining information about the tools that originated sniffed network traffic.…”

Section: Value Of the Datamentioning

confidence: 99%

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Data set and machine learning models for the classification of network traffic originators

et al. 2022

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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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Artificial Intelligence-Based Approaches for Anomaly Detection

Cherukuri,

Ikram,

et al. 2024

Encrypted Network Traffic Analysis

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Encryption-agnostic classifiers of traffic originators and their application to anomaly detection

Cited by 8 publications

References 12 publications

Data set and machine learning models for the classification of network traffic originators

Data set and machine learning models for the classification of network traffic originators

Network traffic classification based‐ masked language regression model using CNN

Artificial Intelligence-Based Approaches for Anomaly Detection

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