A big data analytics approach to combat telecommunication vulnerabilities
| Autor: | Thanh Van Do, André Årnes, Hai Thanh Nguyen, Kristoffer Jensen |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer Networks and Communications
business.industry Computer science Big data Vulnerability 02 engineering and technology Computer security computer.software_genre Telecommunications network Deregulation 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Mobile telephony business Telecommunications Set (psychology) computer Software Computer network |
| Zdroj: | Cluster Computing. 20:2363-2374 |
| ISSN: | 1573-7543 1386-7857 |
| DOI: | 10.1007/s10586-017-0811-x |
| Popis: | Both the telecommunication networks and the mobile communication networks are using the Signaling System No. 7 (SS7) as the nervous system. It allows mobile users to communicate using SMS and phone calls, manage billing for operators and much more. Primarily, it is a set of protocols that allows telecommunication network elements to communicate, collaborate and deliver services to its users. Deregulation and migration to IP have made SS7 vulnerable to serious attacks such as location tracking of subscribers, interception of calls and SMS, fraud, and denial of services. Unfortunately, current protection measures such as firewalls, filters, and blacklists are not able to provide adequate protection of SS7. In this paper, a method for detection of SS7 attacks using big data analytics and machine learning is proposed. The paper clarifies the vulnerabilities of SS7 networks and explains how the proposed techniques can help improve SS7 security. A proof-of-concept SS7 protection system based on big data techniques and machine learning is also described thoroughly. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink