2019
DOI: 10.1088/1742-6596/1353/1/012133
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Method of analyzing computer traffic based on recurrent neural networks

Abstract: The given paper proposes a method of analyzing network traffic based on recurrent neural networks. There overview of perspective approaches for analyzing network traffic in order to detect attacks is provided. The authors investigated the largest and currently the most relevant CICIDS2018 dataset. The methods of dealing with the class imbalance in a dataset by adapting the Focal Loss function to the problem of traffic analysis are considered. There proposed method provides the effective representation of infor… Show more

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Cited by 11 publications
(4 citation statements)
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“…Therefore, considering that Slowloris creates traffic similar to legitimate connections, this detection strategy could lead to false positive errors. A critical challenge for these approaches relies on determining the majority of legitimate traffic behavior that will be applied to the training sessions [39][40][41].…”
Section: Related Workmentioning
confidence: 99%
“…Therefore, considering that Slowloris creates traffic similar to legitimate connections, this detection strategy could lead to false positive errors. A critical challenge for these approaches relies on determining the majority of legitimate traffic behavior that will be applied to the training sessions [39][40][41].…”
Section: Related Workmentioning
confidence: 99%
“…In the next paper, Chastikova and Sotnikov [14] proposed a long-short term memory (LSTM) [15] model to analyse network traffic. Even though the work was just theoretical, it is interesting that the utilisation of the focal loss function [16] was mainly used in the area of computer vision to address the imbalance in the distribution of classes in the dataset.…”
Section: State Of the Artmentioning
confidence: 99%
“…Furthermore, variations of an original experiment may be performed on the same dataset. However, providing [17] 97.00 n/a n/a n/a Chastikova and Sotnikov [18] n/a n/a n/a n/a D'hooge et al [ these scores may be valuable for future comparative research. Table 3 provides an alphabetical listing by author of the papers discussed in this section, along with the proposed respective model(s) for CICIDS2018, and Table 4 shows the same ordered listing by author coupled with the associated computing environment(s) for CICIDS2018.…”
Section: Research Papers Using Cicids2018mentioning
confidence: 99%