Upload
cvidya-networks
View
1.193
Download
1
Embed Size (px)
Citation preview
These are the voyages of cVidya in its quest to battle big data fraud
and to boldly go where no fraud solution has gone before
2
Key Facts
Canada
Brazil
Guatemala
South Africa
Israel Spain
UK Ukraine
India Singapore
Bulgaria USA Macedonia
cVidya is a leading supplier of Analytics solutions to communications and digital service providers. cVidya’s big data technology platform and analytical applications enable operators to optimize profits and enhance decision-making.
160+ customers in 64 countries
300 Employees
Founded 2001
Leading Provider of Analytics Solutions
Business success with proactive revenue assurance (2013)
TM Forum Leadership (2012)
Partner Network Specialized Award (2012)
Revenue Analytics & Fraud Mgmt leader (2012)
Revenue Management leader (2012)
Most innovative vendor (2012)
#1 Revenue Management Global Market (2011)
Serving 7 out of the 10 largest operators in the world
Global Footprint – 13 locations worldwide
Industry Recognition Customer Base & Partnerships
Partnership with leading global vendors
3
In 60 Seconds
Consumption
Payments
Social
Interactions
Location
Retailing
Web Browsing
Apps Usage
60% of online data comes from mobile
4
Interpreting Big Data Hype
When new technologies make bold promises, how do you separate the hype from what's commercially viable? And when will such claims pay off, if at all?
5 5
Big Data Analytics
"Data is widely available, what is scarce is the ability
to extract wisdom" Hal Varian, Chief Economist, Google
6 6
What Do We Provide?…
cVidya provides an analytical platform embedded with
best practices use cases for different purposes such as RA,
FM, Marketing Analytics & Data Monetization - all using
industry standard big data environments
7
New Fraud Challenges
The telecom market is in a dramatic transition phase that influences the fraud department’s challenges and activities
What new types of risks are out there?
What needs to be monitored?
Using what tools?
How do we support the enormous amount of data and find the “needle in the haystack?”
7
8
According to the latest CFCA report (published in 2013) there is a 15% increase in fraud losses (compared to 2011)
PBX hacking, PRS/IRSF, bypass and subscription fraud still cause the industry damages of billions of $ annually
Traditional Fraud is Still a Major Pain
$5.22 B – Subscription Fraud $4.42 B – PBX Hacking $3.62 B – Account Take Over / Identity
Theft $3.62 B – VoIP Hacking $3.35 B – Dealer Fraud
9
Operators need to balance between getting to know the new and emerging types of fraud, and coping with the traditional types that still cause them major damages
9
11
Fraud detection and prevention through DPI
− DPI reveals new areas that up till now weren’t covered - allowing for detection of new types of fraud types and service abuse
− The amount of DPI transactions is tremendous!
− BD capabilities are a must when dealing with DPI information
Some examples of fraud scenarios which can only be detected using DPI:
− Abnormal usage Analysis
− Proxy Fraud - Disguising premium data traffic to avoid additional payments
− IP PBX hacking detection - Toll fraud conducted by fraudsters by compromising corporate IP PBX
− Tethering - Revenue loss to the operator due to sharing of a single Internet connection by several devices
Case:
13
Abnormal Patterns Analysis
The Issue
− Fraudsters commit mobile / e-commerce fraud while accessing websites from their smartphones / tablets
− Mobile / e-commerce companies can only detect fraud attempts on their own websites
The Solution
− A DPI-based solution that enables telcos to monitor and detect the OTT activity of the mobile data user
− The solution looks for suspicious behavior in the entire network
Business Value
− Telcos can offer / share the insights gained from monitoring activity
− Providing mobile / e-commerce companies with insight into fraud committed across the network
− Enables mobile / e-commerce companies to reduce their exposure to fraud
14
Abnormal Patterns Analysis – Use Case
The system characterizes what is defined as “reasonable” usage patterns of a normal user in the network and alerts abnormal behavior
Normal user browses several websites throughout the day, attackers will most probably access only the targeted website)
Number of accesses to specific websites should be reasonable (Multiple accesses to eBay or Amazon are suspicious)
Sequential destination port numbers
A “normal” mobile data user / subscriber profile is based on the DPI component that reveals the applications and services being used by the user
16
Proxy Fraud
Issue Disguising premium data traffic to avoid additional payments to telcos
Need Telcos are moving to advanced billing schemes Detects users that are trying to bypass the billing
processes / avoid additional charges
solution A DPI-based solution that enables telcos to detect such disguised traffic
Business
Value Telcos can recover lost revenues
17
Proxy Fraud (Cont.)
Users connect to proxy services (located outside / beyond the ISP network) that allow them to connect to the requested website preventing the ISP from monitoring and billing this activity.
By using DPI the fraud system can use SSL protocol to detect disguise proxy activity.
The DPI record demonstrates using YouTube using an encrypted protocol and destination IP which doesn’t belong to YouTube subnet
19
IP PBX Hacking Detection
The Issue
− Toll fraud is being performed by compromising corporate IP PBX
− Recent CFCA-reports estimate fraud damage at > $4.96B per annum
The Need
− Organizations are legally liable for fraudulent traffic in their networks and must proactively monitor their PBX activities and detect hacking attempts
The Solution
− An IP probe / DPI device located within the corporate LAN
− The device monitors abnormal PBX port scanning activity
Business Value
− Detects the hacking attempts effectively
− Performs corrective actions to remove all malicious devices within the network
− Prevents PBX hacks / toll fraud
Massive parallel processing
P = Performance
Scalability & linear growth Longer retention time Shorter processing durations Wider back office processing & analysis
C = Cost Reduction in HW & SW licenses
Commodity hardware & storage
Better planning & targeting
High availability
Historical & Real-time data
C = Coverage
Verticals & LOBs
Multiple sources & systems
Multiple departments
Structured & Unstructured
Centralized platform
Multiple user types
20
Big Data Analytics Benefits
- cVidya Big Data Analytics Platform Benefits C2P
22
A new initiative of the TMF - Unified Analytics Big Data Repository (ABDR)
Supports multiple use-case & analytics systems
Data repository of loosely coupled data entities
Standard definition using data dictionary
Benefits
Avoiding data replications
Saving in ETL costs & time
Faster time to implement new use-cases
Open platform
ABDR
23
Real-time Event Queuing
Big Data Architecture
Unified Analytical Layer
Data Node
Data Node
Ad-Hoc Reports
Real-time Streaming Component
Data Node
Map Reduce
Data Node
Business Widgets
Case Management
…
cVidya’s Unified Analytics
Business & Operational Dashboards
Premodeled Customer Data
Applications
Columnar Data Base (Optional)
MoneyMap® Plus| FraudView® Plus | Enrich™ | Engage™
cVidya’s Big Data Platform
Real-time Comparison
Advanced Analytical Models
All Data Sources CRM
Mediation ERP
IP&DPI Probes Switch
Billing DWH Order & Provisioning
cVidya’s ETL
24
Why cVidya
cVidya is leading the way with Big Data
Expanded RA, Fraud and Analytics products to
support big data based infrastructures
− Leveraged the latest Big Data technologies to
enable enormous data volume processing and
advanced analytics
− Leading the TMF ABDR project - Analytics Big
Data Repository