Survey on Big Data Analysis Algorithms for Network Security Measurement
| Autor: | Hanlu Chen, Yulong Fu, Zheng Yan |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Focus (computing)
Network security business.industry Computer science Big data Data classification 020206 networking & telecommunications 02 engineering and technology computer.software_genre Data science Field (computer science) Reduction (complexity) 0202 electrical engineering electronic engineering information engineering Key (cryptography) Malware 020201 artificial intelligence & image processing business computer Algorithm |
| Zdroj: | Network and System Security ISBN: 9783319647005 NSS |
| DOI: | 10.1007/978-3-319-64701-2_10 |
| Popis: | With the development of network technologies such as IoTs, D2D and SDN/NFV, etc., convenient network connections with various networks have stepped into our social life, and make the Cyber Space become a fundamental infrastructure of the modern society. The crucial importance of network security has raised the requirement of security measurement on a heterogeneous networking system. However, the research on this topic is still in its infancy. According to the existing security evaluation schemes of intrusion and malware detection, we believe the network data related to security should be the key for effective network security measurement. A study of the algorithms in terms of data analysis for Data Dimension Reduction, Data Classification and Data Composition becomes essential and urgent for achieving the goal of network security measurement. In this paper, we focus on the problem of big data analysis methods for security measurement, and mainly investigate the existing algorithms in different processes of big data analysis. We also evaluate the existing methods in terms of accuracy, validity and their support on security related data analysis. Through survey, we indicate open issues and propose future research trends in the field of network security measurement. |
| Databáze: | OpenAIRE |
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