AI Threat Identification with Dynamic Weighting and Feedback
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Solution Overview
Problem
Current information security systems are non-predictive, non-adaptive, and non-dynamic, failing to effectively identify complex cyber threats due to the lack of AI/ML-based weighted factors and closed-loop systems that adapt to changing attack vectors.
Innovation Solution
A system implementing artificial intelligence-based, dynamic, predictive statistical classification meta-algorithms that record real-time network traffic, manage client actions across all security protocol layers, and recalibrate weights based on variance analysis to create a closed-loop system for improved threat identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current signature-based security systems are used, then implementation is simple, but they cannot effectively identify complex and evolving cyber threats
Solution Approach 1:
The patent implements dynamic weighting factors that automatically adjust the importance of different security algorithms based on real-time threat detection performance. The system continuously recalibrates weights to optimize threat identification, transforming static signature-based detection into a dynamic, adaptive system that evolves with emerging threats.
Solution Approach 2:
The system changes parameters by introducing multiple adjustable weighting factors (w1, w2, w3, etc.) that control the contribution of different security algorithms. These parameters are dynamically modified based on variance analysis and threat detection results, allowing the system to adapt its behavior without complete reconfiguration.
2Adaptability or versatility
If static security algorithms are used, then system operation is simple, but they are not adaptive to changing attack vectors
Solution Approach 1:
The patent implements a closed-loop feedback system where threat detection results feed back into the weighting mechanism. The system calculates variance between detected threats and expected patterns, then uses this feedback to automatically adjust algorithm weights, creating a self-adapting security system that improves over time without manual intervention.
Solution Approach 2:
The system performs self-calibration by automatically adjusting its own weighting factors based on performance metrics. The variance calculation and weight recalibration occur autonomously, allowing the security system to service itself and adapt to new threats without requiring external reconfiguration or expert intervention.
3Measurement precision
If multiple AI/ML algorithms are combined with dynamic weighting, then threat detection precision improves, but computational complexity increases
Solution Approach 1:
The patent manages algorithmic complexity by introducing a hierarchical parameter structure where multiple weighting factors control different aspects of algorithm combination. This parameterized approach allows precise control over the complexity-precision tradeoff, enabling the system to adjust its computational intensity based on threat levels and available resources.
Data Source
AI summary
A system includes a compute engine that implements artificial intelligence-based dynamic, predictive, statistical classification meta-algorithms for better information security threat identification. The closed loop output of this algorithm uses a dynamic weights model along with AI/ML/DL to identify diverse information security threats more comprehensively and more efficiently. This system evolves with time and self-corrects to adapt to the ever changing needs of cyber security.