Abstract
Big data is a hot topic in the current academia and industry circles, which is influencing people’s daily lifestyles, work habits and ways of thinking. Due to the complexity of data itself and the huge amount of data, big data faces many problems in the process of collection, storage and use. It requires a new processing model to have greater decision making, insight and process optimization capabilities to accommodate massive, high growth rates and diverse information. The strategic significance of big data is not to master huge data information, but to conduct specialized analysis and processing of these meaningful data. This paper focuses on the analysis of IP network traffic under big data, and studies the sources of existing network traffic, the purpose of traffic analysis, and the common analysis methods for big data traffic. The structure and usability of Hadoop-based traffic analysis framework are mainly studied, and a new prospect is proposed for the future development direction.
This work is supported by the Fundamental Research Funds for the Central Universities (HEUCFG201827, HEUCFP201839).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Apache Zookeeper. http://zookeeper.apache.org/
Chandramouli, B., Goldstein, J., Duan, S.: Temporal analytics on big data for web advertising. In: 2012 IEEE 28th International Conference on Data Engineering, pp. 90–101. IEEE (2012)
Dede, E., et al.: MARISSA: MapReduce implementation for streaming science applications. In: 2012 IEEE 8th International Conference on E-Science (e-Science), pp. 1–8. IEEE (2012)
Falsafi, B., et al.: Deep analytics (2011)
Gubanov, M., Pyayt, A.: MEDREADFAST: a structural information retrieval engine for big clinical text. In: 2012 IEEE 13th International Conference on Information Reuse and Integration (IRI), pp. 371–376. IEEE (2012)
Kang, U., Chau, D.H., Faloutsos, C.: PEGASUS: mining billion-scale graphs in the cloud. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5341–5344. IEEE (2012)
Ketata, I., Mokadem, R., Morvan, F.: Biomedical resource discovery considering semantic heterogeneity in data grid environments. In: Hruschka, E.R., Watada, J., do Carmo Nicoletti, M. (eds.) INTECH 2011. CCIS, vol. 165, pp. 12–24. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22247-4_2
Lee, Y., Kang, W., Lee, Y.: A hadoop-based packet trace processing tool. In: Domingo-Pascual, J., Shavitt, Y., Uhlig, S. (eds.) TMA 2011. LNCS, vol. 6613, pp. 51–63. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20305-3_5
Lee, Y., Lee, Y.: Detecting DDoS attacks with Hadoop. In: Proceedings of the ACM CoNEXT Student Workshop, p. 7. ACM (2011)
Lee, Y., Lee, Y.: Toward scalable internet traffic measurement and analysis with Hadoop. ACM SIGCOMM Comput. Commun. Rev. 43(1), 5–13 (2013)
Lee, Y., Kang, W., Son, H.: An internet traffic analysis method with MapReduce. In: Network Operations and Management Symposium Workshops (NOMS Wksps), 2010 IEEE/IFIP, pp. 357–361. IEEE (2010)
Verma, A., Cherkasova, L., Kumar, V.S., Campbell, R.H.: Deadline-based workload management for MapReduce environments: pieces of the performance puzzle. In: 2012 IEEE Network Operations and Management Symposium (NOMS), pp. 900–905. IEEE (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Yin, H., Sun, J., Shi, Y., Sun, L. (2019). IP Network Traffic Analysis Based on Big Data. In: Liu, S., Yang, G. (eds) Advanced Hybrid Information Processing. ADHIP 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 279. Springer, Cham. https://doi.org/10.1007/978-3-030-19086-6_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-19086-6_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19085-9
Online ISBN: 978-3-030-19086-6
eBook Packages: Computer ScienceComputer Science (R0)