Real-Time Emerging Threat Detection With Dynamic Feature Vectors
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Solution Overview
Problem
Conventional AI systems struggle to identify emerging threats in real-time due to reliance on static models trained on historical data, lack of computational resources, and inability to generalize to new or dynamically changing environments, leading to inefficiencies and inaccuracies in threat detection.
Innovation Solution
A method and system using an AI model that computes proximity of feature vectors based on frequency, recency, pattern, and intensity dimensions, classifying events into predefined categories, and identifying emerging threats by comparing with historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional AI systems use static models trained on historical data, then they can process large volumes of data efficiently, but they fail to identify emerging threats and generalize to new situations
Solution Approach 1:
The patent transforms static threat detection models into dynamic systems that continuously adapt to new threats. The system employs evolving feature vectors that capture temporal patterns and relationships, allowing the model to dynamically adjust to emerging threats rather than relying on fixed historical patterns. This dynamic approach enables the system to maintain high productivity while improving adaptability to new situations.
Solution Approach 2:
The system changes key parameters by introducing time-dependent feature vectors with multiple dimensions (frequency, recency, pattern, intensity) instead of static features. These parameter changes allow the model to capture evolving threat characteristics and generalize to new situations while maintaining efficient processing through structured dimensional comparisons.
2Reliability
If conventional AI systems rely on extensive server setups and expansive datasets, then they can achieve comprehensive threat analysis, but they lack rapid response capability and require significant computational resources
Solution Approach 1:
The patent extracts and focuses on the most critical dimensions for threat detection (frequency, recency, pattern, intensity) from complex datasets, rather than processing entire expansive datasets. This extraction approach maintains reliable threat detection by concentrating on key discriminative features while reducing computational overhead and enabling faster response times.
Solution Approach 2:
The system performs partial analysis by comparing feature vectors across selected dimensions rather than comprehensive analysis of all available data. This partial action approach achieves sufficient detection reliability for emerging threats while significantly reducing computational requirements and response time compared to exhaustive analysis methods.
3Measurement precision
If conventional AI systems use pre-defined patterns and characteristics, then they can detect known threats effectively, but they struggle to generalize to unforeseen risks and dynamically changing environments
Solution Approach 1:
The patent adds temporal and relational dimensions to threat detection by using multi-dimensional feature vectors that capture frequency, recency, pattern, and intensity over time. This dimensional expansion allows the system to maintain precision for known threats while gaining the ability to detect and generalize to unforeseen risks through temporal pattern recognition.
Solution Approach 2:
The system creates a universal feature vector framework that can detect both known and emerging threats through the same multi-dimensional comparison mechanism. This universal approach allows the model to function effectively across diverse threat types and changing environments, combining the precision of pattern recognition with the adaptability of temporal analysis.
Data Source
AI summary
The disclosure relates to a method and system for identifying emerging threats in real-time using Artificial Intelligence (AI) model. The method includes receiving first set of feature vectors created from content; determining first set of dimensions for each of first set of feature vectors; comparing first set of dimensions, for each of first set of feature vectors, with second set of dimensions associated with each of second sets of feature vectors created for historical events; computing degree of proximity of first set of feature vectors relative to each of second sets of feature vectors through proximity analysis; identifying contemporaneous to receiving first set of feature vectors, second set of feature vectors from second sets of feature vectors; classifying event into event category from predefined event categories based on computed degree of proximity and predefined threshold; and identifying event as emerging threat.