Anomaly detection system for mobile carrier based on Big Data concept
DOI:
https://doi.org/10.20535/2411-1031.2016.4.1.96016Keywords:
Anomaly detection, Big Data concept, information security, data analysis, machine learning, cellular communication, signature detection.Abstract
The continuous growth of information technologies in the modern world has caused a gradual increase in data circulating in the information and telecommunication systems, which in turn generates a large number of new threats, that is not so easy to detect. Standard methods of detection based on the signature method, which is comparing the traffic coming into the network with databases of known threats. However, these methods are ineffective when the threat is new and it has not yet been added to the database. In this case, it is necessary to use a more intelligent methods, which are able to monitor any unusual activity for a particular system – the methods of anomaly detection. Particularly, this problem is actual for mobile operators that have recently often face different types of fraud (leakage international traffic, false billing), which is impossible to determine in real time. Therefore, it is appropriate to implement in carrier’s network intelligent system that is able to process large amounts of data in real time and warn about possible threats. However, known threats will be faster detected by signature module, so it is logical to include it in system. The performance of the system will be provided using the methods and tools of Big Data, concretely by using a distributed file system and parallel computing on multiple servers will dynamically process data. That anomaly detection system was developed in this paper.
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