Applications of sketches in network traffic measurement: A survey

Han, Hui; Yan, Zheng; Jing, Xuyang; Pedrycz, Witold

doi:10.1016/j.inffus.2021.12.007

Cited by 19 publications

(2 citation statements)

References 90 publications

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“…No additional hardware detection is required. This method achieves a higher detection rate compared to other existing methods [12] [15]. In summary, many scholars and scientists have invested a lot of efforts to enhance the prediction accuracy of network traffic recognition and applied it to multiple fields.…”

Section: Related Workmentioning

confidence: 94%

Network Traffic Identification Based on Improved EM Algorithm

Cui,

Liang,

Wang

2024

IEEE Access

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With the continuous increase and complexity of network traffic, traditional network traffic recognition technology is facing numerous difficulties, especially in dealing with outlier data and improving recognition accuracy. Therefore, an improved expectation maximization algorithm based on the constraint matrix Z and Tsallis entropy is proposed. The core goal of this algorithm is to accelerate the convergence of classification and improve accuracy. Furthermore, to enhance the classification accuracy, the spatial expectation maximization algorithm is introduced, which innovatively converts the sample mean and covariance matrix into 1 L -median and modified rank covariance matrix. According to the experimental data, therecall rate of the original expectation maximization algorithm is only 74%. However, the recall rate of the spatial expectation maximization algorithm in the ATTACK service has significantly increased to 85%.In other tests, such as WWW and P2P services, the recall rate has also significantly improved, increasing from 96% and 95.3% to 97.7% and 96.1%, respectively. These experimental results highlight the superior robustness of the spatial expectation maximization algorithm in handling outlier data. It further proves the outstanding performance in improving the accuracy of network traffic recognition. This research has brought significant innovation and potential practical value to the network traffic identification.

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Section: Related Workmentioning

confidence: 94%

Network Traffic Identification Based on Improved EM Algorithm

Cui,

Liang,

Wang

2024

IEEE Access

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“…Present-day big data situations heavily distort data streams including real-time IP traffic and network click streams [1][2]. Recording the frequency of flowing content in high-speed networks and locating the top-k major flows is a difficult undertaking [3].…”

Section: Introduction 11 Backgroundmentioning

confidence: 99%

ActiveHolder : an accurate and fast sketch for finding top-K flows

Lin

Qin

Nie

2023

Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022)

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The fundamental issue in network traffic measurement is data stream processing, and the main technique is estimating the frequency of various things to determine the top-k elephant flow. Finding top-k flows is crucial for the use of network management technologies including traffic engineering, DDoS attack detection, network anomaly detection, and congestion control. The expansion of high-speed networks has led to an increase in network traffic, making it important to create a quick and accurate online identification top-k method that uses less bandwidth. Our research revealed that it is challenging for current algorithms to simultaneously attain high precision and high throughput when memory is limited. To address this issue, we propose a brand-new data structure called ActiveHolder that combines counter and sketch technology and uses a replacement decay scheme to maintain the frequency values of the top-k flow items. This significantly reduces the impact of mouse flow on elephant flow. The experimental findings demonstrate that the ActiveHolder algorithm outperforms the state-of-the-art technology by achieving high precision and high processing rates with minimal memory cost.

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