Network Topology Integrated Behavioral Analysis for Cybersecurity
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Solution Overview
Problem
Existing network security methods are limited in scope and data processing capabilities, making it difficult to detect and mitigate cyber threats by monitoring for anomalous user behavior across multiple information sources.
Innovation Solution
A system and method that combines user and entity behavioral analysis (UEBA) with network topology information to provide improved cybersecurity risk characterizations. This involves gathering network entity information, establishing behavioral baselines, monitoring for anomalies, and incorporating network topology to evaluate cybersecurity risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network security methods are used to monitor for anomalous behavior, then basic threat detection is possible, but the scope of information analyzed is limited and data processing capabilities are insufficient
Solution Approach 1:
The patent combines multiple information sources including user behavioral data, entity behavioral data, network topology information, and vulnerability data into a unified risk assessment model. This integration allows the system to overcome the limitations of traditional single-source monitoring by analyzing correlations across diverse data types, thereby improving threat detection accuracy without proportionally increasing system complexity
Solution Approach 2:
The system employs a multi-functional architecture where the same analytical engine processes various data types (behavioral logs, network topology, vulnerability databases) and performs multiple functions including anomaly detection, risk scoring, and threat prioritization. This universal approach enables comprehensive analysis across multiple information sources using a single coordinated system rather than separate specialized tools
2Reliability
If multiple sources of information are analyzed together to improve threat detection, then more comprehensive security analysis is achieved, but data processing requirements and system complexity increase
Solution Approach 1:
The system performs preliminary processing of multiple information sources by establishing baseline behavioral profiles for users and entities before actual threat detection occurs. Network topology and vulnerability data are pre-loaded and structured in advance. When anomalies occur, the system can quickly compare against pre-computed baselines and pre-structured data, significantly reducing real-time processing requirements while maintaining comprehensive multi-source analysis
Solution Approach 2:
The patent applies different processing depths and analytical methods to different data sources based on their specific characteristics and relevance to the current analysis context. Not all data sources are processed with equal intensity at all times - the system dynamically adjusts the level of analysis applied to each information source based on its current relevance and the specific threat scenario being investigated
3Measurement precision
If network topology information is incorporated into behavioral analysis, then cybersecurity risk characterization is improved, but the complexity of the analysis model increases
Solution Approach 1:
The patent segments the overall risk assessment model into distinct modular components: behavioral analysis modules that process user and entity data separately, network topology analysis modules that handle graph data structures, and vulnerability assessment modules that evaluate security weaknesses. Each module operates independently on its specialized data type and produces standardized output that feeds into the integrated risk score, making the complex overall model manageable through modular design
Data Source
AI summary
A system and method for network cybersecurity analysis that uses user and entity behavioral analysis combined with network topology information to provide improved cybersecurity. The system and method involve gathering network entity information, establishing baseline behaviors for each entity, and monitoring each entity for behavioral anomalies that might indicate cybersecurity concerns. Further, the system and method involve incorporating network topology information into the analysis by generating a model of the network, annotating the model with risk and criticality information for each entity in the model and with a vulnerability level between entities, and using the model to evaluate cybersecurity risks to the network. Risks and vulnerabilities associated with user entities may be represented, in part or in whole, by the behavioral analyses and monitoring of those user entities.