Data-dependent metric filtering

Mic, Vladimir; Zezula, Pavel

doi:10.1016/j.is.2021.101980

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…After capturing the network traffic data, due to the huge amount of network traffic data [17], and some botnet traffic and duplicate content, effective measures must be taken to filter the traffic. Through reasonable filtering means, the traffic data can be more conducive to subsequent network security detection [18]. This paper uses the KNN algorithm to filter traffic in the traffic filtering sub-module under the feature selection module.…”

Section: Traffic Filtering Of Knn Classification Algorithmmentioning

confidence: 99%

Network Security Detection Method Based on Abnormal Traffic Detection

Xiao,

Ke,

YiWen

et al. 2023

IJACSA

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To discover potential risks and vulnerabilities in the network in time and ensure the safe operation of the network, a network security detection method based on abnormal traffic detection is studied. Construct network security detection architecture from several aspects, including the front-end interface module, control center module, network status extraction module, anomaly detection module, alarm module, and database module. Use NetFlow technology to capture network traffic from the network in the form of flow, and use the KNN algorithm in the traffic filtering submodule to filter network traffic packets and eliminate duplicate traffic data. After filtering traffic, the traffic data is transmitted to the feature selection sub-module. PCA-TS algorithm is used to reduce the dimension of the network traffic data and select the network traffic characteristics, and then it is input into the SVM classifier. The improved SVM multi-classification algorithm is used to classify normal and abnormal traffic, complete abnormal traffic detection, and achieve network security detection. Experimental results show that the time for feature selection of this method does not exceed 3.0s, and the G score in the detection process also remains above 0.70, indicating that this method has strong network security detection capability.

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Section: Traffic Filtering Of Knn Classification Algorithmmentioning

confidence: 99%