Security Resource Prioritization Using Path Deviation Detection
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Solution Overview
Problem
Computing devices face limitations in providing security management services due to finite hardware resources, which can lead to unmitigated security threats by failing to identify individuals posing a risk within dense environments.
Innovation Solution
A system prioritizes security management services based on the likelihood of individuals posing a security risk by tracking and comparing their paths to typical patterns, reallocating resources to those deviating from expected routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security management services are provided to all individuals in dense environments, then security coverage is improved, but hardware resource consumption increases beyond available limits
Solution Approach 1:
The system applies different levels of monitoring intensity to different individuals based on their risk profiles. High-risk individuals receive intensive monitoring with multiple sensors and analysis, while low-risk individuals receive minimal or no monitoring. This local differentiation of quality allows the system to maintain security coverage for all individuals while concentrating hardware resources only where needed, resolving the contradiction between comprehensive security and resource limits.
Solution Approach 2:
The system dynamically changes the parameter of resource allocation based on inferred security risk levels. By using machine learning models to assess risk parameters and adjusting monitoring intensity accordingly, the system transforms a static resource allocation problem into a dynamic one where resources are optimized based on real-time risk assessment, enabling both comprehensive coverage and resource efficiency.
2Measurement precision
If computing resources are allocated to track and analyze all individual paths, then detection accuracy is improved, but processing time and resource consumption increase
Solution Approach 1:
The system extracts only the critical subset of path data that is necessary for security assessment. Instead of analyzing all movement data from all individuals with equal depth, the system uses machine learning models to identify and focus analysis on paths that deviate from normal patterns or exhibit suspicious characteristics. This extraction of essential information maintains detection accuracy while dramatically reducing processing time and resource consumption.
Solution Approach 2:
The system applies partial analysis to most individuals and excessive (intensive) analysis only to those exhibiting suspicious behavior. By using a two-tiered approach where routine paths receive minimal processing and anomalous paths receive intensive scrutiny, the system achieves high measurement precision for security-critical cases without the time penalty of exhaustive analysis across all individuals.
3Reliability
If more sensors and computing power are deployed to monitor all individuals, then security detection capability is improved, but device complexity and cost increase
Solution Approach 1:
The system uses a universal machine learning-based risk assessment framework that can operate with varying levels of sensor input depending on the environment and threat level. The same core system can function with minimal sensors in low-risk scenarios and scale to utilize multiple sensor types and computing resources when threats are detected. This multi-functionality allows the system to maintain high detection capability without permanently requiring complex hardware configurations.
Solution Approach 2:
The system dynamically adjusts its operational complexity based on real-time threat assessment. Rather than maintaining fixed high-complexity configurations, the system scales its sensor utilization and computing power according to the inferred risk level. This dynamic approach allows the system to achieve high reliability when needed while avoiding the permanent device complexity and cost associated with always-maximum configurations.
Data Source
AI summary
Methods and systems for providing security management services are disclosed. To provide security management services in a manner that reduces the quantity of hardware resources necessary to provide the security management services, the security manager may prioritize security management services based on the likelihood of individuals posing a security risk. To identify the security risks of the individuals, the paths of the individuals may be tracked and compared to typical paths through environments that are followed by most individuals that traverse through the environment. The individuals following a typical path may not indicate a security risk and a resource prioritization may be retained or decreased for the individuals not posing a security risk. The individuals not following a typical path may indicate a security risk and a resource prioritization may be increased for the individuals posing a security risk.