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Unsupervised Anomaly Detection in Mobile Networks

Ranjkeshzadeh, Sina | 2021

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 54485 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Amini, Arash; Kazemi, Reza
  7. Abstract:
  8. With the fast-growing mobile networks, the need for network reliability increases. Various events like antenna failures, attacks on mobile networks, floods, sporting events cause abnormal behavior in mobile networks. Some events like antenna failures and attacks need consideration. But some of them like sporting events can be ignored. Right now, most mobile operators, detect these events manually which is a very time-consuming and costly task for them. To overcome this problem one idea is to predict future data by prediction algorithms. Then compare new data with the predicted one. If there exists significant deviation, there is an anomaly. But most of the failures and anomalies in mobile networks happen at minor components of the network data like only for a data package or a specific location. They do not affect the network's total data significantly. So these anomalies are ignored automatically. But they can be a sign of an important event like an attack. In this paper, we introduce a new method for unsupervised anomaly detection in mobile networks applicable on voice, SMS, and Data which finds all of the anomalies of the network and discards non-important ones. This method is additionally applicable to other networks like power grids and financial data
  9. Keywords:
  10. Anomaly Detection ; Deep Learning ; Machine Learning ; Mobile Networks ; Telecommunication Traffic

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