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Big Data Analysis for Anomaly Detection in Telecommunication Using Clustering Techniques
Published in Springer Singapore
Volume: 862
Pages: 111 - 121

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. © Springer Nature Singapore Pte Ltd. 2019.

About the journal
JournalData powered by TypesetAdvances in Intelligent Systems and Computing Information Systems Design and Intelligent Applications
PublisherData powered by TypesetSpringer Singapore
Open AccessNo