How to identify user actions from phone number events?

[suhashini25]

Identifying user actions from phone number events involves correlating communication data (calls, SMS, data usage tied to a phone number) with other available logs and contextual information. This process is complex and often requires access to sensitive data, making legal and ethical considerations paramount, especially under data protection laws like Bangladesh's upcoming Personal Data Protection Act (PDPA).

This analysis is typically performed by:

Telecom Providers: For network management, service quality, fraud detection, and billing.
Law Enforcement/Forensic Investigators: With appropriate legal authorization, to reconstruct timelines and establish connections in criminal investigations.
App Developers (limited and anonymized): To understand app usage patterns, usually without direct access to specific phone numbers of end-users for privacy reasons.
Here's how user actions can be identified or inferred from phone number events:

1. Types of Phone Number Events:
Call Events:
Outgoing Calls: Phone number dialed, timestamp (start/end), duration, call status (connected, failed).
Incoming Calls: Phone number of caller, timestamp, duration, call status (answered, missed, rejected).
SMS/MMS Events:
Sent/Received Messages: Sender/recipient phone number, timestamp, message status (sent, delivered, read), (potentially) message content.
Data Usage Events:
Session start/end times, data volume used (tied to the subscriber's phone number).
Network Events:
Registration/Deregistration: When the phone attaches/detaches from the network.
Location Updates: Handover between cell towers (Cell ID, Location Area Code).
2. Identifying User Actions (Inference Techniques):
The process involves correlation and pattern recognition across different data sources.

A. Timestamp Correlation with App/System Logs:

App Usage: If a user makes an outgoing call immediately romania phone number list after opening a ride-sharing app or a delivery app, it might indicate they are contacting their driver/delivery person. Similarly, an SMS received with an OTP (One-Time Password) followed by a login attempt on a banking app points to an authentication action.
Device Activity: Matching call/SMS times with screen on/off times, app foreground/background states, or sensor data (e.g., GPS activation for navigation apps) can infer user engagement.
Account Changes: A series of incoming SMS (e.g., porting confirmation codes, deactivation notices) around the time of an account change (like a SIM replacement or number portability request) strongly indicates a user initiating or being subject to an account management action.
B. Analysis of Associated Numbers/Content (where permissible):

Known Service Numbers: Calls to specific short codes or known customer service numbers of banks, utility companies, or e-commerce platforms indicate the user is interacting with that service.
SMS Keywords: While full content analysis is highly restricted, detecting common keywords (e.g., "OTP," "code," "confirm," "delivery," "booking") in SMS metadata (if permitted) can strongly infer specific transactional actions. For instance, an incoming SMS containing an OTP followed by an outgoing SMS to a certain number could indicate a money transfer.
Contact Names (from device data): If device call logs are available, associating phone numbers with saved contact names provides direct context about who the user was communicating with.
C. Network Event Context:

Location Inference (from Cell IDs): Sequences of cell tower changes associated with data usage or call patterns can infer user movement, travel patterns, or presence at specific locations (e.g., office, home, public places). This can be correlated with the user's phone number activity to understand actions taken in specific geographic contexts.
Roaming Status: Changes in network registration from home to foreign networks indicate international travel.
D. Behavioral Analytics and Anomaly Detection:

Unusual Activity: A sudden surge in international calls, an abnormally high number of SMS to premium rate numbers, or multiple SIM replacement requests within a short period, can be flagged as anomalous phone number events that might indicate fraudulent user actions (e.g., SIM swap attacks, illegal calls).
Pattern Deviation: Changes from a user's typical communication patterns (e.g., calls exclusively to unknown numbers, calls at unusual hours) can suggest compromise or unusual behavior.
E. App Permissions and API Interactions:

For mobile forensics, understanding which apps have permissions to read call logs or send SMS, and analyzing how those apps interact with system telephony APIs, can reveal if an app itself is initiating actions or logging data related to phone numbers.
3. Data Sources for Analysis:
Call Detail Records (CDRs) from Telecom Operators: The most comprehensive source for all communication events tied to a phone number.
Device Call Logs and SMS Databases: Extracted from the user's mobile device (requires authorization/forensic tools).
App-specific Logs/Databases: If accessible (requires root/jailbreak or app-specific exploits/backups), these provide insight into communication within specific apps.
Network Logs: From cell towers, base stations, and core network elements.
Privacy and Legal Framework:
It is crucial to reiterate that accessing and analyzing phone number events and user data without explicit consent or a lawful basis (e.g., court order for a specific investigation, consent for legitimate service provision, fraud detection) is a violation of privacy and illegal. Laws like the Bangladesh Personal Data Protection Act (PDPA), when fully enacted, will impose strict requirements on how personal data, including phone numbers and associated activity, is collected, stored, processed, and analyzed. Organizations must adhere to principles of data minimization, purpose limitation, transparency, and user rights when performing such analysis.