Multi-Layer ML Prediction Engine for Accurate Cyber-Threat Detection
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Solution Overview
Problem
Existing ML systems struggle to accurately predict and mitigate cyber-threats by considering a comprehensive range of behavioral data from systems and users, often leading to incomplete or inaccurate threat assessments.
Innovation Solution
A multi-layered ML model with input, intermediary, and output prediction layers, each comprising multiple ML models, aggregates and processes diverse data to provide a more accurate prediction of cyber-threats, enabling effective mitigation strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single-layer ML model is used for threat prediction, then the system complexity is low, but the prediction accuracy is insufficient
Solution Approach 1:
The patent divides the ML model into multiple layers (input prediction layer, intermediary prediction layers, and output prediction layer), where each layer processes specific aspects of behavioral data independently before aggregating results. This segmentation allows each sub-model to focus on particular features, improving overall prediction accuracy while maintaining manageable complexity through modular design
Solution Approach 2:
The patent introduces an additional dimensional structure by stacking multiple prediction layers vertically, transforming a flat single-layer model into a hierarchical multi-layer architecture. This dimensional change enables the system to process data through multiple stages of abstraction, enhancing predictive capability without linearly increasing operational complexity
2Measurement precision
If multiple ML models are aggregated across multiple layers, then the prediction accuracy improves, but the computational resources required increase
Solution Approach 1:
By segmenting the computational workload across multiple specialized layers, each processing specific types of behavioral data, the system avoids redundant computations that would occur in a monolithic model. Each layer can be optimized independently for its specific task, reducing overall computational waste
Solution Approach 2:
The patent merges the outputs of multiple independent ML models through aggregation mechanisms that combine predictions from different layers. This merging allows the system to leverage the strengths of multiple models simultaneously while sharing computational infrastructure, reducing total resource consumption compared to running separate independent systems
3Reliability
If comprehensive behavioral data from multiple sources is collected, then the threat detection completeness improves, but the data processing complexity increases
Solution Approach 1:
The patent segments comprehensive behavioral data into distinct categories (user behavior, system behavior, network traffic) and processes each category through specialized ML models in different layers. This segmentation allows the system to handle diverse data types independently using appropriate processing methods for each, improving detection completeness while managing complexity through specialized handling
Solution Approach 2:
The multi-layer architecture serves multiple functions simultaneously: the input layer processes raw data, intermediary layers perform feature extraction and pattern recognition, and the output layer generates predictions. This multi-functionality allows a single system to handle diverse data types and processing requirements without requiring separate dedicated systems for each function
Data Source
AI summary
Described herein is a prediction engine for aiding decision support. In some examples, the prediction engine can be used in aiding cyber security applications. The prediction engine can include multiple prediction layers that each include a number of machine learning models that contribute to an overall prediction of the prediction engine in predicting whether a respective system or system user poses a cyber-threat. The prediction engine can provide prediction data that can indicate that the respective system or system user is a cyber-threat. In some examples, a decision engine can be employed to use the prediction data to mitigate or eliminate the cyber-threat.