Mavenir: Linking IoT, 5G, and Analytics in Smarter Enterprise
Mavenir: Evolution of Real-Time Machine Learning (RRML) in Core Network Security and Anti-Fraud Into 5G and New Services
1. Copyright Mavenir 2019. Proprietary and Confidential. mavenir.com
Evolution of Real-Time Machine
Learning (RRML) in Core Network
Security and Anti-Fraud Into 5G and
New Services
2. Context
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Supply Chain
Privacy and Data protection
Signaling
Cloud
Internet of Things
Human
Device
Security
Threat
Landscape
Global
Fraud
Survey
Global loss – $29.2B
Identity fraud
PBX hacking
IRSF
By-pass
Arbitrage
Wholesale
Challenge
Expertise
Technology
(5G)
Organization
3. 5G PPP Security requirements
Higher security and privacy level
Security Automation
Security Monitoring
Security Management
Liability schemes
Slice isolation
End to end encryption
Regulation
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Any security trade-
off would be
misused by an
attacker in form of
Fraud or damage
5. 5G security Architecture
• 5G reference architecture
offers comprehensive security
but still could be exploited
– Implementation trade-offs
– Multi-vendor environments
– IT technology
• Typical Fraud scenarios are
not (fully) covered
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User Application Provider Application
SN
HE
3GPP AN
Non-3GPP AN
(I) (I)
(I)
(II)
(IV)
(V)
Application
Stratum
Home Stratum/
Serving Stratum
Transport Stratum
(II)
(I)
(III)
(I)
ME USIM
(I)
SEPP AUSF ARPF SEAF
6. Why Telco needs help ?
MNO
• Security is a cost factor
contradicting OPEX KPI
• Fraud team is often detached from
the network
• Regulation
• Marketing
• International risks/supply chain
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Banking
• Security = Anti-Fraud
• Security = Main business
• Security is embedded into
technology
• Direct law enforcement
VS
7. Mavenir approach
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Expertise
• Hands-on understanding
of vulnerabilities
• Business drivers
• Creativity
Technology
• Machine Learning and AI
• Deep understanding of
protocols
Service
• Knowledge
• Protection
• Security as a Service
8. Machine Learning for Security solutions
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Business
and
Technical
data
Technology Proficiency
Controlled
AI
Enable and Support the Telco Transformation
9. Key technical principles for Machine Learning in Telco
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Specialized/tailored algorithms
Real-Time factor (response time in under 10ms)
Subject area focus rather then generic data focus
Seamless integration of Machine Learning and rule engine
5G and Service Mesh architecture readiness
Horizontal approach (focus beyond fraud and security)
10. Extending the role of NWDAF into security and Anti-Fraud
SMSF
NWDAF
Constant insight
Real-time ML
Reaction to anomalies
Fraud prevention
Attack detection
Security assurance
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Mavenir Security Suite
Network/Voice
Module
Subscription
Fraud
Messaging Fraud/
SpamShield
Wholesale
Fraud IoT Security Credit Scoring
Firewall layer: SS7, ISUP, Diameter, SIP, GTP, HTTP/2
Robocall
control
AI and Machine Learning engines
• Single Platform, Single architecture, multiple modules
• Leveraging NWDAF architecture
• Natively developed with Real-time Machine Learning
• Fraud prediction based on ML
• Rule-based configuration integrated with ML
• Dedicated algorithms for major use-cases
• Flexible reporting and real-time dashboards
• Multiple integration options
• NFV & Microservices architecture
Real-time Fraud prevention and detection
Optimal OPEX for mobile operators
Revenue savings proven in the market
Regulator compliancy
Efficient subscriber protection
Future proof
Data
Anti-Fraud
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Machine Learning / AI for Automation and Insights
Insight
IoT
Profile 1
IoT
Profile 2
IoT
Profile 3
Profile Change Pattern
Network
Function
RuleEngine
Mediation
Featurization
Machine Learning
Learning Module
Reporting &
Investigation
Alert
Management
FMS Analytics
Additional
Data
Feeds
Standard market deployed architecture addressing:
DDoS detection and IoT service anomaly detection, Voice & Messaging Fraud, Signaling and
Service protection
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