The number of worldwide mobile phone users is expected to pass the five billion mark in 2019, while the total number of connected devices will likely reach as many as 29 billion by 2022. In this environment, mobile networks face a constantly increasing number of challenges related to network and subscriber security.
One of the key issues for mobile networks is SS7 and Diameter network vulnerabilities. All mobile networks communicate with each other through the widely-used SS7 protocols and most of mobile phone user traffic such as phone calls and SMS messages is routed via SS7. Unfortunately, intruders with an Internet connection and a few simple tools can pose multiple threats to almost all SS7 networks and their subscribers.
Recent research indicates that as many as 89% of mobile networks are vulnerable to fraud and interception of subscriber phone calls, SMS messages, and personal information from mobile applications such as Facebook Messenger and WhatsApp. Research also points out that up to an extent, all networks are susceptible to threats such as disclosure of personal subscriber information (including their location, IMSI, and device IMEI) and denial of service for particular subscribers or parts of the network.
Elitnet’s Mobile Network Security solution addresses the above mobile network and subscriber security vulnerabilities by passively monitoring SS7 and Diameter network traffic. The solution uses a constantly expanded knowledge database to detect currently prominent threats as well as state-of-the-art machine learning methods to detect network anomalies and previously unknown threats to SS7 network and subscriber security. All anomalies and threats detected by the solution are showcased on a convenient graphical user interface with powerful report generation, data visualization, and drilldown capabilities.
The solution is implemented as a collection of data processors/applications for Elitnet’s Data Analytics Platform, a high-performance data analysis platform which covers data collection from multiple sources, Artificial Intelligence/Machine Learning enabled data processing, and powerful reporting and monitoring tools.
KEY DETECTED THREATS
PERSONAL INFORMATION
• Detection of subscriber’s location.
• Retrieval of SIM card’s IMSI.
• Retrieval of device IMEI.
TRAFFIC INTERCEPTION
• Interception / recording of incoming calls.
• Interception / recording of outgoing calls.
• Interception of messages.
DENIAL OF SERVICE
• Attempts to disconnect a subscriber’s device from the network.
• Attempts to block access to a base station.
• Attempts to take advantage of SIGTRAN vulnerabilities and disrupt service in the entire network.
INTERNET OF THINGS
• Detection of anomalies in traffic related to IoT terminals to prevent any attacks on network’s IoT infrastructure and end-user devices. Real-time network monitoring enables instant anomaly detection.
NON-STANDARD ACTIONS
• Attempts to take over a device and force it to carry out non-standard actions (paging requests, silent calls, binary SMS, zero-content SMS, etc.).
• Attempts to infect devices and take advantage of data security weaknesses (unusually large number of data sessions, increased data usage, etc.).
PREVIOUSLY UNKNOWN THREATS
• New threat recognition and identification of anomalies and deviations from standard network behavior by using methods of mathematical statistics and machine learning.
RELATED PRODUCTS
Big Data and AI-enabled network
security, fraud prevention, and
predictive maintenance