Abstract
As people use the network more and more and release more and more personal information to the Internet, it also caused the leakage of personal information. According to the above background, the optimization research on the classification detection method of network anomaly data was proposed. Correlation analysis was carried out for the conventional algorithm, and the related model was constructed. A new algorithm was proposed to detect the network anomaly data to improve the processing ability of the network anomaly data. The experimental data showed that the proposed network anomaly data classification detection optimization algorithm improved the processing range by 31% when processing abnormal data, and the efficiency of processing data was increased by 36%. It proved the effectiveness of the new method and provided a theoretical basis for the processing of future abnormal data.
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References
Nemanja, B., Duan, K.Z.: Improved real-time data anomaly detection using context classification. Expert Syst. Appl. 23(14), 23–36 (2017)
Sun, Y., Wong, A.C., Kamel, M.S.: Classification of imbalanced data: a review. Int. J. Pattern Recognit. Artif. Intell. 12(3), 501–520 (2017)
Wang, B., Zhao, Y., Hu, F., et al.: Anomaly detection with subgraph search and vertex classification preprocessing in Chung-Lu random networks. IEEE Trans. Signal Process. 66(20), 5255–5268 (2018)
Hussain, J., Lalmuanawma, S., Chhakchhuak, L.: A two-stage hybrid classification technique for network intrusion detection system. Int. J. Comput. Intell. Syst. 9(5), 863–875 (2016)
Xin, D.U., Huang, X., Hongga, L.I., et al.: Research on classification of plant community using projection pursuit learning network algorithm on high resolution remote sensing images. J. Geo-Inf. Sci. 18(1), 124–132 (2016)
Al-Obeidat, F., El-Alfy, E.S.M.: Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols. Pers. Ubiquitous Comput. 2, 1–15 (2017)
Nguyen, H.A., Choi, D.: Application of data mining to network intrusion detection: classifier selection model. Lecture Notes in Computer Science 36(12), 230–241 (2017)
Pang, M., Hao, X.: Traffic flow prediction of chaos time series by using subtractive clustering for fuzzy neural network modeling. In: Second International Symposium on Intelligent Information Technology Application, vol. 2(13), pp. 12–26 (2017)
Jerome, R.B., Hätönen, K.: Anomaly detection and classification using a metric for determining the significance of failures. Neural Comput. Appl. 28(6), 1–11 (2016)
Ying, W.: Wireless network traffic abnormal data information detection simulation. Comput. Simul. 34(9), 408–411 (2017)
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Liu, B. (2019). Research on the Classification Method of Network Abnormal Data. In: Gui, G., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 301. Springer, Cham. https://doi.org/10.1007/978-3-030-36402-1_27
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DOI: https://doi.org/10.1007/978-3-030-36402-1_27
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