AI Voice Call Screening With SIP Authentication for Vishing
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Solution Overview
Problem
Vishing attacks exploit the anonymity of telecommunications technologies, particularly VoIP systems, to deceive individuals into revealing sensitive information, with existing security measures being inadequate in detecting and blocking fraudulent calls, especially from new or spoofed numbers, and lacking integration and real-time proactive protection.
Innovation Solution
An integrated system utilizing SIP header customization, STIR/SHAKEN framework, third-party databases, AI/ML engines, and the Viterbi algorithm to verify caller identity, analyze call data for anomalies, and convert speech to text for real-time pattern detection, blocking fraudulent calls before they reach the recipient.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security systems and caller identification technologies are used, then existing infrastructure can be maintained, but they fail to detect or block fraudulent calls effectively
Solution Approach 1:
The system segments the security verification process into multiple independent components: STIR/SHAKEN framework for digital certificate validation, AI/ML engine for pattern recognition, Viterbi algorithm for speech analysis, and anomaly detection modules. Each component handles a specific aspect of fraud detection, improving overall reliability while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary verification service that acts as a mediator between the calling party and the called party. This service validates caller identity through digital certificates and coordinates with AI/ML engines to determine whether to block calls, thereby improving detection effectiveness without requiring direct integration between all system components.
2Reliability
If reactive blocking of known scam numbers is implemented, then reported fraud cases can be addressed, but new or previously unreported numbers cannot be blocked proactively
Solution Approach 1:
The system performs preliminary actions by pre-training AI/ML models with extensive fraud patterns and pre-configuring STIR/SHAKEN verification before fraudulent calls occur. This enables the system to proactively recognize and block new scam numbers immediately upon detection, eliminating the time delay inherent in reactive approaches.
Solution Approach 2:
The patent implements continuous feedback loops where blocked calls and detected anomalies are fed back to the AI/ML engine for learning and model updating. This feedback mechanism enables the system to adapt to new fraud tactics in real-time, maintaining proactive prevention capabilities against emerging scam patterns without manual intervention delays.
3Area of stationary object
If multiple networks and service providers are involved in call routing, then global coverage is achieved, but tracing the origin and verifying authenticity becomes difficult
Solution Approach 1:
The STIR/SHAKEN framework provides a universal verification mechanism that functions across all participating networks and service providers regardless of geographic location. Digital certificates and SIP header validations work consistently across international boundaries, enabling origin verification and authenticity validation throughout the entire call routing path without being limited by network-specific implementations.
4Loss of energy
If VoIP systems are used by scammers, then call cost is reduced for fraudsters, but conventional monitoring and tracking systems are bypassed
Solution Approach 1:
The patent replaces conventional mechanical tracking systems with digital certificate-based verification and AI-driven analysis. Instead of relying on traditional telephone network tracking mechanisms that VoIP bypasses, the system uses STIR/SHAKEN digital signatures and machine learning models to verify caller identity and detect fraud patterns, maintaining tracking effectiveness regardless of the underlying communication infrastructure.
Data Source
AI summary
Systems and methods detect and prevent vishing attacks through an integrated framework combining SIP header customization, STIR/SHAKEN frameworks, AI/ML analysis, and real-time speech analysis using the Viterbi algorithm. The system begins with call initiation, embedding authentication information in the SIP header. The SIP data is transmitted and verified using STIR/SHAKEN frameworks, ensuring the authenticity of the caller's identity. Verified data is cross-referenced with third-party databases and analyzed by an AI/ML engine to detect anomalies. If potential fraud is detected, the call is blocked, and the customer is notified. Calls that pass initial checks are further analyzed using the Viterbi algorithm, which converts speech to text and identifies suspicious patterns. An anomaly pattern detector processes the converted text to detect vishing indicators, terminating the call if a match is found. This multi-layered approach ensures robust protection against vishing, enhancing the security and reliability of voice communications while safeguarding users from fraud.