Interactive AI for Detecting Synthetic Network Activity
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Solution Overview
Problem
The increasing sophistication of synthetic technology makes it difficult to detect synthetic voices and chats, compromising the security of verbal and textual communications, as these can mimic human interactions for malicious purposes, leading to a lack of real-time monitoring for synthetic and malfeasant activity.
Innovation Solution
A system utilizing interactive artificial intelligence that receives communications from end-point devices, determines synthetic likelihood and malfeasance values, and implements restrictions or additional security measures when these values exceed thresholds, employing machine learning models and honey-pot communications to differentiate between human and synthetic interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If synthetic technology is used to mimic human interactions, then the ability to replicate human communication is improved, but the security and detectability of real human interactions deteriorates
Solution Approach 1:
The system performs preliminary detection of synthetic activity before allowing account access or transactions. By analyzing communications in real-time and calculating synthetic likelihood values beforehand, the system can prevent malicious synthetic interactions from compromising account security, thus addressing the security deterioration caused by advanced synthetic technology.
Solution Approach 2:
The patent introduces an intermediary detection system that sits between the synthetic communication tool and the target account. This intermediary analyzes the communication patterns, calculates synthetic likelihood values, and determines malfeasance values to distinguish synthetic interactions from genuine human interactions, thereby maintaining security without compromising the ability of synthetic technology to function.
2Adaptability or versatility
If advanced synthetic technology is deployed, then the sophistication of synthetic voices and chats is improved, but the difficulty of detecting synthetic activity increases
Solution Approach 1:
The system implements feedback mechanisms by continuously analyzing communication patterns and updating synthetic likelihood values based on observed behavior. The machine learning models learn from ongoing interactions, adjusting their detection criteria to adapt to increasingly sophisticated synthetic technology, thereby maintaining detectability despite improving synthetic capabilities.
Solution Approach 2:
The detection system is designed to be dynamic rather than static. It continuously adapts its detection criteria and thresholds based on evolving synthetic technology patterns. The system adjusts its sensitivity and analysis methods in real-time to match the sophistication level of encountered synthetic activity, ensuring that detectability keeps pace with technological advancement.
3Measurement precision
If real-time monitoring is implemented to detect synthetic activity, then the security detection capability is improved, but the system complexity increases
Solution Approach 1:
The monitoring system is segmented into distinct functional modules: communication reception, synthetic likelihood calculation, malfeasance determination, and account protection actions. Each module performs a specific function, making the overall complex system manageable and maintainable while achieving high detection precision through specialized processing in each segment.
Solution Approach 2:
The system employs multi-functional machine learning models that perform multiple detection tasks simultaneously. The same analytical framework calculates both synthetic likelihood values and malfeasance determinations, reducing overall system complexity while maintaining comprehensive security detection capability across different types of synthetic threats.
Data Source
AI summary
Systems, computer program products, and methods are described herein for detecting synthetic activity in a network via use of interactive artificial intelligence are provided. The method includes receiving a first account communication from an end-point device. The first account communication is associated with a user. The method also includes causing a transmission of a first reply communication based on the first account communication to be provided to the end-point device. The method further includes receiving a second account communication. The method still further includes determining a synthetic likelihood value based on the first account communication and/or the second account communication. The synthetic likelihood value indicates the likelihood that the first account communication and/or second account communication are synthetic. The method also includes causing a restriction in access to one or more accounts associated with the user in an instance in which the synthetic likelihood value is above a synthetic threshold.