Cyber-Physical Graphs for Autonomous Threat Mitigation
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Solution Overview
Problem
Current cybersecurity solutions are inadequate in addressing the complexity and frequency of cyber attacks, as they require active configuration and ongoing administrator interaction, providing limited protection against sophisticated adversaries and failing to holistically manage cybersecurity vulnerabilities across enterprises.
Innovation Solution
A system for advanced cybersecurity threat mitigation using behavioral and deep analytics, which includes a time series data store, an action outcome simulation module, an observation and state estimation module, and a directed computational graph module, to monitor network events, simulate scenarios, and produce security recommendations based on analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cybersecurity solutions are used, then they provide basic protection against known threats, but they require active configuration and ongoing administrator interaction, providing limited protection against sophisticated adversaries
Solution Approach 1:
The cybersecurity system performs self-service by automatically monitoring network events, analyzing behavioral patterns, and generating security recommendations without requiring active administrator configuration. The system autonomously updates its understanding of normal vs. malicious behavior through continuous data collection and analysis, eliminating the need for manual rule updates while maintaining high protection effectiveness against sophisticated threats.
Solution Approach 2:
The system performs preliminary action by establishing baseline behavioral patterns of network resources before attacks occur. By pre-characterizing normal behavior through continuous monitoring and analysis, the system can rapidly detect deviations indicating cyber threats without requiring reactive configuration changes during incidents, thereby providing proactive protection while minimizing administrator intervention.
2Device complexity
If traditional cybersecurity solutions are used, then they are simpler to implement, but they fail to holistically manage cybersecurity vulnerabilities across enterprises
Solution Approach 1:
The cybersecurity system achieves universality by implementing a comprehensive multi-functional platform that simultaneously performs network event monitoring, behavioral baseline establishment, anomaly detection, threat analysis, and security recommendation generation. This holistic system manages cybersecurity vulnerabilities across entire enterprises through integrated analysis of multiple data sources including network traffic, system logs, and security events, providing comprehensive protection rather than isolated security functions.
Solution Approach 2:
The system applies segmentation by dividing the enterprise network into monitored resources with individual behavioral baselines while maintaining centralized coordination. Each network resource (servers, workstations, applications) is analyzed as a separate entity with its own normal behavior profile, allowing the system to detect localized threats while maintaining holistic enterprise-wide security management through aggregated analysis of all segmented components.
3Measurement precision
If high-volume network data is monitored, then detection capability is improved, but data processing complexity and resource requirements increase
Solution Approach 1:
The system applies partial action by focusing computational resources on analyzing only the most significant behavioral deviations from established baselines rather than processing every network event in detail. By identifying and prioritizing anomalies that represent potential threats while using sampled or summarized data for routine monitoring, the system maintains high detection precision for critical threats while reducing overall data processing complexity and resource requirements through selective deep analysis.
Data Source
AI summary
A system for mitigation of cyberattacks employing an advanced cyber decision platform comprising a time series data store, a directed computational graph module, an action outcome simulation module, and observation and state estimation module, wherein the state of a network is monitored and used to produce a cyber-physical graph representing network resources, simulated network events are produced and monitored, and the network events and their effects are analyzed to produce security recommendations.