User Language Modeling for Deepfake and Vishing Detection
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Solution Overview
Problem
Existing systems struggle to effectively detect and mitigate phishing, vishing, and deep fake attacks in real-time electronic communication sessions, particularly due to the sophistication and variability of these threats, which can compromise network security by deceiving users into divulging information.
Innovation Solution
A machine learning-based system analyzes user language patterns from previous communication sessions to identify potential imposters, using text, voice, and video analysis to detect anomalies and secure communication sessions by requiring authentication or taking action against suspected threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring of electronic communications is implemented, then detection accuracy can be improved, but it becomes impossible to monitor millions of communications in real-time
Solution Approach 1:
The patent replaces manual monitoring with an automated machine learning system that analyzes communication patterns, language usage, and behavioral characteristics to detect phishing, vishing, and deepfake attacks. The system processes millions of communications by substituting human analysis with algorithmic detection mechanisms that evaluate multiple features simultaneously.
2Device complexity
If traditional security methods are used, then implementation is simpler, but they cannot detect sophisticated and evolving attacks
Solution Approach 1:
The system dynamically adjusts detection parameters and thresholds based on learned patterns from training data. It evaluates multiple features including language characteristics, communication timing, user behavior patterns, and contextual information. The machine learning model adapts to new attack types by retraining on updated datasets, maintaining effectiveness against evolving threats.
Solution Approach 2:
The patent combines multiple detection approaches into a unified system that analyzes text, voice, video, and metadata together. It integrates various machine learning models and analysis techniques to create a composite detection framework that is more robust than individual methods alone.
3Reliability
If real-time analysis of all communications is performed, then security coverage is improved, but computational resources and time consumption increase
Solution Approach 1:
The system applies partial analysis by focusing computational resources on communications that exhibit suspicious characteristics. It uses preliminary filtering to identify potential threats and then applies more intensive analysis only to those cases. This selective approach maintains high security coverage while reducing overall computational burden compared to analyzing every communication in full detail.
Solution Approach 2:
The system performs preliminary training and pattern recognition in advance, building models from historical data before real-time detection. It pre-processes and stores characteristic patterns of legitimate and malicious communications, enabling faster real-time classification without requiring full re-analysis of all training data during active monitoring.
Data Source
AI summary
Language used by a specific user in a specific context is gathered. The language used by the specific user in the specific context is language gathered from a plurality of previously captured electronic communication sessions. For example, the language of the specific user is captured from previous voice, video, and/or text communication sessions. A machine learning process based on the language gathered from the plurality of previously captured electronic communication sessions is trained. The trained machine learning process is used to determine if the specific user is actually participating in an electronic communication session or if a potential imposter is likely posing as the specific user in the electronic communication session. In response to determining that the potential imposter is likely posing as the specific user in the electronic communication session, an action is taken to secure the electronic communication session.