Abstract
In order to improve the security of the distributed optical fiber sensing network, the self-adaptive detection of the fiber sensing network needs to be carried out, and an overlap detection algorithm under the APT attack of the distributed optical fiber sensing network based on the spectral characteristic component and the big data analysis is proposed. the large data sampling model of the network APT attack is constructed, the attack characteristics and the related properties of the distributed optical fiber sensing network virus are simulated by adopting the spectrum correlation characteristic detection and the large-data quantization characteristic coding, and the large-data fusion and feature extraction of the APT attack information are realized, the output abnormal characteristic detection of the distributed optical fiber sensing network is carried out through the feature extraction result, a distributed optical fiber sensing network intrusion large data statistical analysis model is constructed, and a narrow-band signal spectrum offset correction method is adopted, And calculating the connection probability density and the individual infection probability of the APT attack node, and improving the detection capability of the network APT attack. The simulation results show that the algorithm can effectively implement the network APT attack detection, improve the security detection capability of the network APT attack, and has a good network security protection capability.
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2019 Guangdong Higher Education Teaching Reform Project “Research on Network Database Learning Based on Learning Behavior Big Data Visualization”; 2019 Huali College Guangdong University of Technology Project “Research on Network Database Learning Based on Learning Behavior Big Data Visualization” (GGDHLYJZ[2019]No.32).
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Fan, Gg., Zhai, Jl. (2020). Network APT Attack Detection Based on Big Data Analysis. In: Zhang, YD., Wang, SH., Liu, S. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 326. Springer, Cham. https://doi.org/10.1007/978-3-030-51100-5_30
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DOI: https://doi.org/10.1007/978-3-030-51100-5_30
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