Name: Identification of Nomadic Targets

Text: ISS Webinar – 13th may 2008
Identification of Nomadic
Presented by Vincent BARRY
VP Global Sales

Solutions to current Challenges
What is the common Telco/ISP investigative link?
The electronic ‘DNA’
• CDR’s, IP Metadata, RADIUS logs

A identifier, B Identifier, Operators, Cell (or ISP) Location, Time on/off, other more
technical info (trunk ID’s, Server IP addr, bytes sent/rec, MAC addr, type of service)

Using Intelligent Systems to analyse massive amounts of data
– CDR/SMS/IP-Metadata/IP-Logs

Ability to profile target behaviour
Identify multiple SIM/Handset/Device usage
Rules engines to extract patterns and known Identifiers
Easy to use – not assume technical genius at k/b
Data to Charting - fast - to display complex device pattern, relationships

Location Tracking System- GSM/CDMA

Passively track ‘visiting’ subscribers – identify specific roamers/country/target ID
Alerts on known devices/numbers
Geo Fencing against known/new mobiles and areas of interest/concern

How we can identify targets

Profiling on Telecommunications identifiers
Internet account & login
•Account: abu_bakar
•Password: ab430456

Multiple Sims

IP address
MAC address

PSTN phone
Multiple Handphones

We can track a specific target through ALL his electronic
communications - if the data is available from the Operators.
We can
• detect change of SIM and change of handset after identifying 1
suspect Number
• We can even detect that profile again even if the phone AND
SIM are changed – (Call fingerprinting)

Compares Telecomms behaviour of a Target or device with stored
historical profile
Searches for deviations from normal behaviour
Uses ThorpeGlen patented processes -statistical engine data analysis


Target has passed his phone onto someone else.
= Target’s behaviour has changed.
Output Layer

Call Count

Time of Day

Day of Week

Orig Cell

Dest Cell


Probability / Deviation

Target using multiple SIMS, or changed handset

1. After making request to
Telco for CDR block keyed on 1 Target number.
With 1 click we can produce
a chart to illustrate call/

2. Our system will highlight
other number that has been a
“number of interest” in past

Our system will extract the 2
numbers of interest and examine
for commonality (profile) classic illustration of same target
- different SIM and Handset

By charting CDR’s on 1 known number
our system will automatically display the
relationship of an identified IMIE
(handset) and the multiple SIM’s used
with that Handset (in this example 3
SIM’s)– which is one of the attributes of a
Target Call Profile – then we can look for
other IMIE used with any of those SIMS –
extracting the multiple device

Geographic Profile Example
Comparing CDR Cell ID location of Target with
Location Tracking solution updates - active updates
Overlaying historic
CDR/SMS location

Database listing showing individual
location update entries that can
provide quick access and identify

Profile of location updates –
clusters show target is stationery in
Base Station area during time of
forced “Mobile Location update”

New Developments Finding Cliques (people Cells) within mass data
Identifying Cliques (groups that only talk amongst themselves)
• Everyone on a Telephone network is part of a group
• Most groups talk to other groups/individuals/nodes
• Example we have already researched
– We processed all the CDR’s from all subscribers (1 week) in a Mobile
network we have access to
– Over 1 billion per day x 7 days = 8 billion+ events
– 1 operator 50 m subscribers
– 48 m – 1 large group
– 400k – large nodes (services, shops, info numbers, etc)
– Remainder - we graded the remaining groups into size (largest to
smallest) they ranged in size from 2 to 142 subscribers
– Identified a number of groups that
• only call each other – never to other numbers
• No-one ever calls their numbers
• WHY??

Clique Example

Thank you
Further information and discussion:
Vincent Barry – VP Global Sales
Mobile - +65-97558905

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