Performance analysis of network traffic capture tools and machine learning algorithms for the classification of applications, states and anomalies

Fowdur, Tulsi Pawan; Baulum, B. N.; Beeharry, Yogesh

doi:10.1007/s41870-020-00458-0

Cited by 18 publications

(11 citation statements)

References 17 publications

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“…Analysis of Table 2 shows that the recall rate of network resource information is different under different methods. When the data volume of network resources is 15 GB, the recall rate of reference [6] is 81%, that of reference [7] is 74%, that of reference [8] is 75%, and that of this method is 96%. e overall analysis of the table shows that the average recall rates of reference [6], reference [7], reference [8], and this method are 79.5%, 75.7%, 76.2%, and 95.8%, respectively.…”

Section: Recall Ratio Of Classification Methods Of Networkmentioning

confidence: 96%

“…When the data volume of network resources is 15 GB, the recall rate of reference [6] is 81%, that of reference [7] is 74%, that of reference [8] is 75%, and that of this method is 96%. e overall analysis of the table shows that the average recall rates of reference [6], reference [7], reference [8], and this method are 79.5%, 75.7%, 76.2%, and 95.8%, respectively. is shows that the method in this paper has a good effect of information retrieval of network resources.…”

Section: Recall Ratio Of Classification Methods Of Networkmentioning

confidence: 96%

“…According to the analysis of Table 1, when the data volume of network resources is 5 GB, the precision of reference [6] is 73%, that of reference [7] is 76%, that of reference [8] is 79%, and that of this method is 92%. When the data volume of network resources is 30 GB, the precision of reference [6] is 75%, that of reference [7] is 73%, that of reference [8] is 81%, and that of this method is 89%. e precision rate of this method is always at a high level, which shows that the classification effect of this method is better.…”

Section: Precision Ratio Of Classification Methods Of Networkmentioning

confidence: 99%

“…In addition, the performance of classification processing can be measured by the number of resources processed per unit time. In order to verify the research on the network resource classification method of this paper, we use the methods proposed in reference [6], reference [7], and reference [8] to compare with the method proposed in this paper to verify the performance.…”

Section: Data Acquisition Terminalmentioning

confidence: 99%

“…Fowdur et al proposed a machine learning algorithm for the classification of applications, states, and anomalies [6]. e aim of that paper is to investigate the performance of different network traffic capture tools for extracting features and to evaluate the performance of eight machine learning (ML) algorithms in the classification of (1) applications, (2) states, and (3) anomalies.…”

Section: Introductionmentioning

confidence: 99%

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Research on Classification Method of Network Resources Based on Modified SVM Algorithm

Zhang¹,

Jingchao²,

Zhang

2021

Security and Communication Networks

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According to the traditional classification method of network capital resources, there are some problems such as low efficiency, low recall rate, and low precision rate of information. Therefore, this paper proposes a new classification method of network capital resources based on SVM algorithm. Firstly, the original sample data are analyzed by principal component analysis to realize the design of resource classification process. Then, the dimension reduction of network resources data is realized by word segmentation and denoising. Thirdly, the reduced sample data are trained by the SVM classifier, and the best parameters of the reduced data are obtained by the grid search method. Lastly, the search range of SVM classifier parameters based on the original sample data is reset, so as to quickly obtain the best SVM classifier parameters of the original sample data and realize the classification. The experimental results show that this method can improve the recall and precision of network resource information and shorten the classification time of network resources.

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