Enterprise Cybersecurity Defense System Using ML Correlation
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Solution Overview
Problem
Existing cybersecurity systems fail to effectively detect and mitigate cyberattacks due to their inability to correlate information across multiple domains, leading to compromised operational integrity and potential unauthorized access to data and resources.
Innovation Solution
An Enterprise Cybersecurity Defense System (eCDS) that integrates machine learning-based user behavior and system access models to detect anomalies and implement adaptive mitigation actions, combining identity and access restrictions with predictive detection and active defense mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing detection systems use traditional security measures, then basic security protection is provided, but the ability to correlate information across multiple domains is insufficient leading to missed attacks
Solution Approach 1:
The patent merges multiple security domains (network security, identity management, access control, endpoint protection) into a unified correlation engine that processes events across all domains simultaneously. This integration enables the system to detect attacks that span multiple domains by correlating events that would be invisible to isolated traditional security systems.
Solution Approach 2:
The correlation engine serves multiple functions simultaneously: it correlates events across domains, detects anomalies, identifies attack patterns, and triggers coordinated responses. This multi-functional approach consolidates what would traditionally require separate systems into a single unified platform that addresses both detection reliability and operational complexity.
2Measurement precision
If machine learning models are trained on historical data, then detection accuracy improves, but the system cannot detect zero-day attacks or novel attack vectors
Solution Approach 1:
The system dynamically adapts its detection models through continuous learning from new events and feedback loops. The machine learning components are retrained periodically with fresh data from the correlation engine, enabling the system to detect previously unknown attack patterns while maintaining high precision for known threat types.
Solution Approach 2:
The system incorporates feedback mechanisms where detected anomalies and confirmed attacks feed back into the training data reservoir. This feedback loop enables the machine learning models to continuously refine their detection accuracy and adapt to evolving attack vectors, resolving the tension between precision for known threats and adaptability for novel attacks.
3Speed
If real-time correlation of events across multiple domains is implemented, then attack detection speed improves, but computational resources and processing complexity increase
Solution Approach 1:
The correlation engine applies different processing intensities to different event types and domains based on their security significance. High-priority events from critical domains receive intensive real-time correlation analysis, while lower-priority events undergo lighter processing. This localized quality approach maintains fast detection for critical threats while reducing overall computational resource consumption.
Solution Approach 2:
The system performs partial correlation analysis for most events using efficient algorithms, reserving intensive computational resources only for high-value events that trigger deeper analysis. This selective processing strategy enables real-time detection speed for the majority of events while controlling total computational resource usage through targeted application of processing intensity.
4Loss of time
If automated mitigation actions are implemented, then response time to attacks improves, but false positives may cause unnecessary system disruptions
Solution Approach 1:
The system performs preliminary analysis and correlation of events before automatically triggering mitigation actions. This preliminary step filters out false positives by cross-referencing multiple data sources and verifying attack patterns, ensuring that automated responses are both timely and accurate, thus reducing unnecessary disruptions while maintaining fast response to genuine threats.
Data Source
AI summary
Computer system security is often implemented using rules-based systems (e.g., allow traffic to this network port, deny it for those network ports; user A is allowed access to these files, but not those files). In enterprises, multiple such systems may be deployed, but fail to be able to intelligently handle anomalies that may technically be permissible but in reality represents a high possibility that there is an underlying threat or problem. The present disclosure describes the ability to build adaptive models using machine learning techniques that integrate data from multiple different domains (e.g. user identity domain, system device domain) and allow for automated decision making and mitigation actions that can provide greater effectiveness than previous systems allowed.