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Big Data Analysis for Anomaly Detection in Telecommunication Using Clustering Techniques

  • C. GunavathiEmail author
  • R. M. Swarna PriyaEmail author
  • S. L. Aarthy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 862)

Abstract

The recent development with respect to Information and Communication Technology (ICT) has a very high impact on the social well-being, economic-growth as well as national security. The ICT includes all the recent technologies like computers, mobile-devices and networks. This also includes few people who have the intent to attack maliciously and they are generally called as network intruders, cybercriminals, etc. Confronting these detrimental cyber activities has become the highest priority internationally and hence the focused research area. For this kind of confront, anomaly detection plays a major role. This is an important task in data analysis which helps in detecting these kinds of intrusions. It helps in identifying the abnormal patterns in various domains like finance, computer networks, human behaviour, gene expression etc. This paper focuses on detecting the abnormalities in the telecommunication domain using the Call Detail Records (CDR). The abnormalities are identified using the clustering techniques namely k-means clustering, hierarchical clustering and PAM clustering. The results obtained are discussed and the clustering technique which is suited better in identifying the anomaly accurately is suggested.

Keywords

Big data analytics Anomaly detection Clustering User behaviour analysis 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Information Technology and EngineeringVellore Institute of TechnologyVelloreIndia

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