Audio Threat Detection for Real-Time Vishing Interruption
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Solution Overview
Problem
Current defense mechanisms are inadequate against voice phishing (vishing) attacks, which require user awareness and cannot provide immediate warnings during voice calls or in-person encounters.
Innovation Solution
A system using machine learning models to analyze audio data for threat detection, identifying patterns indicative of vishing attempts, and implementing corrective actions such as alerts, noise cancellation, or interruptions to protect users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If user awareness is used as the defense mechanism against vishing attacks, then users can understand potential threats, but immediate warning and protection during voice calls cannot be provided
Solution Approach 1:
The patent introduces an intermediary system (audio analysis system) that mediates between the vishing attack and the user. This system includes audio capture components, threat pattern recognition engines, and alert generation mechanisms that automatically detect and warn users of vishing attempts during calls, providing immediate protection without requiring user awareness of specific threat patterns
2Reliability
If audio analysis is performed in real-time to detect vishing threats, then immediate threat detection is achieved, but system complexity and processing requirements increase
Solution Approach 1:
The patent implements preliminary action by pre-loading threat pattern databases and pre-configuring audio analysis parameters before actual vishing detection is needed. The system pre-processes and stores known vishing patterns, allowing the real-time detection to focus on comparing incoming audio against these pre-established patterns, thereby reducing real-time computational complexity while maintaining high detection reliability
Solution Approach 2:
The audio analysis system is segmented into distinct functional modules: audio capture module, pre-processing module, pattern recognition module, and alert generation module. This segmentation allows each component to be optimized independently and processed in parallel, reducing overall system complexity while enabling real-time threat detection through distributed processing
Data Source
AI summary
Methods may include receiving, via a computing device associated with a first user, audio data indicative of at least audible speech of a second user and ambient noise. The ambient noise may comprise ambient audible speech. The method may include determining, based on a comparison of the audible speech of the second user and the ambient audible speech, that a threat threshold has been satisfied. The method may include outputting, based on the determination that the threat threshold has been satisfied, an indication of malicious activity.