Design and application of network security situational awareness platform based on big data technology

DOI

10.2991/978-94-6463-304-7_61How to use a DOI?

Keywords

Network Security Situational Platform; Design; Application

Abstract

Network security situational awareness is a crucial technology in the field of cybersecurity, with current research heavily reliant on manual threat analysis. In order to achieve automated security monitoring for large-scale networks, this study proposes a network security situational awareness platform based on big data technology. This platform adopts a modular SOA architecture and utilizes massive heterogeneous data collection, standardized processing, and machine learning modeling to enable intelligent detection of network threats. Simulation experiments demonstrate that the platform can effectively enhance the detection rate of complex attacks, such as Advanced Persistent Threats (APTs). The research designs a scalable situational awareness platform that harnesses the advantages of big data technology to improve network security monitoring and threat analysis from a data-driven perspective. Compared to traditional methods, this research is better suited for large-scale heterogeneous network environments and holds significant importance in advancing data-centric network security defense.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yang Shen
PY  - 2023
DA  - 2023/12/04
TI  - Design and application of network security situational awareness platform based on big data technology
BT  - Proceedings of the 3rd International Conference on Digital Economy and Computer Application (DECA 2023)
PB  - Atlantis Press
SP  - 581
EP  - 587
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-304-7_61
DO  - 10.2991/978-94-6463-304-7_61
ID  - Shen2023
ER  -