Collaborative Phone Reputation System for Dynamic Call Filtering
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Solution Overview
Problem
Existing solutions for managing unsolicited phone calls, such as blacklists, require manual configuration and maintenance, and are inadequate in addressing social engineering attacks and telemarketing violations, especially with the prevalence of mobile devices always being available.
Innovation Solution
A collaborative phone reputation system that uses a compute device and server system to analyze incoming calls based on factors like caller identity, call history, and phone number reputation scores, providing dynamic scoring and notification mechanisms to block undesirable calls, and crowd-sourcing data to update phone number reputations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a blacklist system is used to block unwanted calls, then some unwanted calls can be filtered, but the system requires manual configuration and maintenance which is complex and not adaptive to new threats
Solution Approach 1:
The system automatically updates phone number reputations by analyzing call patterns and user feedback without requiring manual intervention. The reputation server autonomously processes incoming data from multiple compute devices, calculates reputation scores, and maintains the database, eliminating the need for manual blacklist configuration and maintenance while improving filtering reliability
Solution Approach 2:
The system implements feedback loops where user responses to incoming calls (answering, blocking, reporting) are transmitted back to the reputation server. This feedback is used to continuously update phone number reputations, allowing the system to adapt to new threats and patterns automatically, resolving the contradiction between reliability and manual maintenance complexity
2Adaptability or versatility
If a static blacklist is used, then implementation is simple, but it cannot address social engineering attacks or adapt to changing threats
Solution Approach 1:
The system transitions from static blacklist entries to dynamic reputation scores that change over time based on call patterns, user feedback, and behavioral analysis. Phone number reputations are continuously updated rather than remaining fixed, enabling the system to adapt to evolving threats like social engineering attacks while maintaining a manageable complexity through automated processes
Solution Approach 2:
The system performs preliminary analysis of incoming calls by checking phone number reputations before calls are connected to users. By pre-assessing threat levels and preparing appropriate actions (block, warn, allow) based on reputation scores, the system enhances adaptability to threats while reducing the complexity of real-time decision-making during actual calls
3Measurement precision
If phone numbers are blocked based on simple blacklists, then the system is easy to operate, but it blocks legitimate calls and misses malicious ones
Solution Approach 1:
The system uses multiple parameters beyond simple presence/absence in a blacklist, including call frequency, time patterns, user feedback responses, and behavioral indicators to calculate reputation scores. These multiple parameters enable precise assessment of call desirability while the system remains easy to operate because users simply interact with familiar call interface elements (answer, block, report) that automatically feed into the scoring system
Data Source
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AI summary
Various systems and methods for a collaborative phone reputation system are described herein. A system for implementing a collaborative phone reputation system includes a compute device comprising: a call handling module to detect, at the compute device, an incoming call for a user of the compute device; a scoring module to determine a local probabilistic score that the incoming call is desirable for the user; and an execution module to perform an action at the compute device based on the local probabilistic score.