Elitnet’s Data Analytics Platform (DAP) is a high-performance data analysis platform which covers data collection from multiple sources, AI/ML enabled data processing, and powerful reporting and monitoring tools.
The platform provides a set of built-in data processing applications/data processors (such as Fraud Prevention, DDoS Attack Detection, etc.) and is open for creation and deployment of new applications. The platform also provides a rich set of tools for data analytics.
To provide both excellent reliability and outstanding price performance, DAP uses a combination of open source and branded systems for data collection, storage, and processing.
The architecture of DAP is designed to ensure horizontal scalability, high availability, and zero downtime during maintenance, allowing the CSP to easily expand the platform’s capacity without influencing its performance.
The key nodes of the platform include the Data Collectors, which collect network data from various sources, including SS7 traffic (via Dialogic Signaling Stack), real-time data traffic (via Napatech boards), CDRs, log files, and other sources. The data is then processed and pre-filtered by the Rule Engine and passed on to the Data Bus component using Apache Kafka open-source software.
The Stream Processors and Batch Processors analyze the data and search for any anomalies, while the NoSQL Database is used for large-volume data storage. The platform also contains Provisioning, Monitoring, and Alarming components which ensure flexible platform configuration and real-time performance monitoring.
KEY APPLICATIONS
SS7 AND DIAMETER THREAT DETECTION
DAP uses a rule-based approach to detect attempts to intercept personal subscriber data, take over devices, and disrupt service. DAP also addresses previously unknown threats based on anomaly detection.
FRAUD
PREVENTION
Data Analytics Platform analyzes call detail records (CDRs) and identifies a wide range of fraudulent activities based on predefined rules as well as subscriber behavior analysis/learning and subscriber profile classification.
PREDICTIVE NETWORK MAINTENANCE
Using its anomaly detection capabilities, the platform predicts faults in various network components and forecasts necessary network infrastructure expansions due to insufficient capacity.
SERVICE DEGRADATION DETECTION
The platform analyzes service data, measures quality of experience (QoE), and detects anomalies which indicate service degradation, allowing the CSP to predict service problems and prevent service downtime.
DENIAL OF SERVICE PREVENTION
DAP looks for anomalies in data traffic to detect possible distributed denial-of-service (DDoS) attacks, attempts to block access to base stations, and attempts to disconnect devices from the network.
MALWARE
DETECTION
DAP uses both antivirus signature databases and machine learning-based methods such as IP to DN and IP to IP activity classification to detect and prevent attempts to disseminate malware in the network.
TRAFFIC
CLASSIFICATION
The platform can classify both encrypted and unencrypted traffic and provide it to other network infrastructure components to ensure quality of service (QoS) and enable charging according to traffic type.
IoT THREAT
PREVENTION
DAP looks for 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.
For more information about the Data Analytics Platform, please download the product datasheet or contact us using the links provided above.
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