In today's telecom landscape, building a robust network infrastructure that can handle, manage, optimize, monitor, and troubleshoot multi-technology and multi-vendor networks is a significant challenge. This need becomes even more pronounced due to the ever-increasing complexities of managing radio networks, core networks, services, transport, and IT operations.

With customers demanding flawless service and high availability, any delay in troubleshooting or errors can lead to customer attrition. To address these challenges, leading Communications Service Providers (CSPs) are turning to advanced anomaly detection solutions powered by Artificial Intelligence (AI) to automate network operations and achieve a holistic view for real-time incident detection.

Digital Transformation Agenda in Network Behavior Analysis

Recent global telecommunication research has highlighted several key points that shape the digital transformation agenda:

1. Strategic Priorities

• Digital Business Models (DBM's) - 71%
• Customer Experience (CX) - 61%
• Cost Control and Business Efficiencies - 53%

The focus on improving Customer Experience (CX) is well understood. However, operators must also innovate business models to unlock efficiencies and create winning experiences in both legacy and new service domains.

2. Process Automation

Process automation and analytics are emerging as long-term enablers for proactive security and Customer Experience improvements. With process automation, the focus is mainly on cost reduction and driving monetization and growth. Emerging technologies like Software-Defined Networks (SDN) and Machine Learning (ML) are expected to drive long-term efficiency gains.

3. Innovation Drivers and Barriers

Analytics and virtualization are seen as the top innovation drivers. However, legacy IT systems and a talent gap act as brakes on innovation. Emphasizing APIs and developer engagement will be crucial for future digital business models.

A Holistic Approach to Advanced Security Intelligence and Network Behavior Analysis

To build new digital and operational models effectively, telecom operators need ubiquitous analytics and advanced security intelligence. This involves:

• Network Operations and Behavior: Capacity planning, real-time optimization, network monitoring, protection, and fault detection.
• Operations: Improve Customer Experience (CE), SLA management, billing, and revenue assurance.
• Customer: Personalized services, plan optimization, social analysis.
• Marketing: Targeted content, campaign management, upsell, and cross-sell.
• Digital Services: Selling customer data to third parties, partner performance management, AI-managed services.

Achieving a sophisticated strategy necessitates several improvement steps, including speeding up analytics initiatives, removing barriers to use case extensions, and widening analytics competencies through dynamic partnerships.

Future Mobile Networks and AI

As global mobile data traffic is projected to skyrocket, with 5G subscriptions expected to reach 3.5 billion by 2026, ensuring that network infrastructure is responsive to a range of use cases is paramount. Future networks will see higher traffic generated by AI-powered machines and bots, autonomous vehicles, and surveillance systems, making AI indispensable for network management.

AI plays a vital role in optimizing existing networks, improving operations, and building future networks. By leveraging AI, telcos can reduce operating costs and enhance performance across their businesses, particularly in the domains of network monitoring and anomaly detection.

Core Elements of Telecom Infrastructure

Self-Organizing Networks (SON) embody three main functionalities essential for modern radio networks:

1. Self-Configuration

This automation aids in network element deployment with minimal human intervention by configuring devices based on embedded software and network data.

2. Self-Optimization

Optimizes network performance through automatic tuning based on performance measurements.

3. Self-Healing

Identifies and resolves network issues dynamically and automatically to ensure stability.

These functionalities enable telcos to manage network operations effectively, even in complex environments, ensuring minimal disruption and optimal performance.

Network Behavior Anomaly Detection

A reliable network demands real-time monitoring and protection against anomalies – sudden, abnormal changes in network behavior. This is achievable through AI-based Anomaly Detection (AD) systems that continuously process large datasets, known as 'Big Data,' to identify any deviations.

Understanding Anomaly Detection

AD is about identifying patterns that deviate from the norm. These could be signs of security threats, hardware malfunctions, or atypical network behavior. High-dimensional data poses challenges in anomaly detection, and AI helps address these by learning from extensive data to predict and spot anomalies effectively.

Applications of Anomaly Detection

AD systems are particularly useful in:

• Reducing application error turnover time
• Ensuring high performance
• Identifying and reacting to security threats proactively

These benefits are crucial for telecom operations, where unexpected spikes or decreases in network performance need immediate attention. AI and ML facilitate democratized solutions, enabling diverse teams to leverage anomaly detection, from business analysts to software engineers.

Advanced Security Intelligence-Based Solutions

While ADS (Anomaly Detection Systems) use domain-specific expertise to identify anomalies, AI-based solutions provide more flexibility. These systems collect vast data volumes, identify abnormal patterns, and either alert experts or trigger automated responses. Popular ADS techniques include:

• Knowledge-Based Systems: Utilizing expert-identified anomaly patterns to automate detection.
• Regression: Forecasting expected behavior and flagging significant deviations as anomalies.
• Classification: Grouping data points to identify deviations from expected clusters.
• Clustering: Using unsupervised learning to group data, identifying outliers as anomalies.

Case Studies in Anomaly Detection

1. Vodafone's Anomaly Detection Application on Google Cloud Platform (GCP)

With Nokia Bell Labs, Vodafone implemented an anomaly detection service on GCP, aimed at detecting and addressing network issues such as congestion and interference. This solution forms part of Vodafone’s strategy to optimize network planning and operations, driving significant efficiencies.

Business Challenges and Key Drivers

Vodafone transitioned from an on-premises Big Data Platform to GCP to overcome data lakes' inefficiencies and improve analytics. The partnership with Nokia brought robust algorithms and shared vision, creating a foundation for future collaboration.

Key Benefits

• Rapid deployment of applications across regions
• Improved operational efficiency (25–30%)
• Enhanced root-cause analysis capabilities

2. E-ADF from Ericsson’s Global Artificial Intelligence Accelerator (GAIA)

Ericsson's E-ADF framework facilitates faster anomaly detection prototyping, making AI tools accessible for anomaly detection. This framework includes algorithms for both univariate and multivariate data and supports high-dimensional data processing efficiently.

Conclusion

AI and ML technologies offer a promising frontier for network traffic monitoring and anomaly detection in the telecom industry. By setting realistic expectations and integrating AI-powered ADS, telecom operators can effectively detect and mitigate network anomalies, ensuring robust and efficient network performance in an increasingly complex digital landscape.