Multi-Level Ensemble Classifiers for Proactive Malicious Domain Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cybersecurity solutions are reactive and struggle to timely address constantly evolving cyber threats, relying on human analysts and single-level machine learning classifiers that are limited in scope and do not provide a plug-and-play architecture for formulating new security models.
Innovation Solution
An Enhanced Predictive Security System (EPSS) employing a domain-centric approach with multi-level machine learning architecture, utilizing ensemble classifiers and improved neutral data sets to predict malicious domains before they become problematic, enabling rapid and repeatable generation of cybersecurity threat analysis applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single-level machine learning classifier is used for cybersecurity threat detection, then the system is simpler to implement, but the detection accuracy and scope are limited
Solution Approach 1:
The patent divides the machine learning system into multiple hierarchical levels: Level 1 uses multiple classifiers trained on different data subsets, Level 2 aggregates their results, and Level 3 performs final classification. This segmentation allows each level to specialize in specific aspects of threat detection, improving overall accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent creates a composite classification system by combining multiple diverse machine learning classifiers (different algorithms, training data, and parameter sets) into an ensemble architecture. This composite approach leverages the strengths of individual classifiers while compensating for their weaknesses, achieving superior detection accuracy compared to any single classifier alone.
2Measurement precision
If human security analysts manually assess security threats, then the analysis can be thorough and adaptive, but the response time is too slow to address constantly evolving threats
Solution Approach 1:
The patent implements self-service through automated machine learning models that independently analyze security threats without requiring continuous human intervention. The system automatically trains classifiers on new data, adapts to emerging threats, and generates security assessments autonomously, maintaining high analysis quality while dramatically improving response speed to evolving threats.
Solution Approach 2:
The patent replaces the mechanical process of manual human analysis with automated machine learning systems. The multi-level ensemble classifiers perform threat assessment through computational algorithms rather than human cognition, enabling faster processing of security data while maintaining or improving analysis quality through systematic pattern recognition.
3Reliability
If blocklists and firewall security are used to mitigate cyber threats, then known malicious domains are blocked, but the approach is reactive and damage has already occurred by detection time
Solution Approach 1:
The patent implements preliminary action by training machine learning classifiers on historical security data and threat patterns before new threats materialize. The system proactively identifies indicators of compromise and potential malicious domains in advance, enabling security teams to take preventive measures before actual attacks occur, rather than reacting after damage has been done.
Solution Approach 2:
The patent incorporates feedback mechanisms where the multi-level classification system continuously learns from new security incidents and threat intelligence. The model updates its parameters and retraining occurs based on feedback from detected threats, allowing the system to adapt and improve its predictive capabilities over time, reducing detection delays for emerging threats.
4Measurement precision
If multiple machine learning classifiers are trained on different data subsets and combined in an ensemble, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the ensemble system into hierarchical levels where Level 1 classifiers process different data subsets independently, Level 2 aggregates their outputs, and Level 3 performs final classification. This segmentation manages complexity by organizing multiple classifiers into a structured hierarchy rather than a monolithic system, making the complex model more interpretable and maintainable.
Data Source
AI summary
Methods, systems, and techniques for producing and using enhanced machine learning models and computer-implemented tools to investigate cybersecurity related data and threat intelligence data are provided. Example embodiments provide an Enhanced Predictive Security System, for building, deploying, and managing applications for evaluating threat intelligence data that can predict malicious domains associated with bad actors before the domains are known to be malicious. In one example, the EPSS comprises one or more components that work together to provide an architecture and a framework for building and deploying cybersecurity threat analysis application, including machine learning algorithms, feature class engines, tuning systems, ensemble classifier engines, and validation and testing engines. These components cooperate and act upon domain data and feature class vectors to create sampled test, training, and validation data and to build model subsets and applications using a trained model library, which stores definitions of each model subset for easy re-instantiation.