Security Management System Using Forensic Path Analysis
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Solution Overview
Problem
In distributed environments like airports, the finite quantity of hardware resources limits the ability to track and analyze the activities of all individuals, potentially missing security risks due to resource constraints.
Innovation Solution
A system that prioritizes security management services by tracking and comparing the paths of individuals to typical paths, using an inference model trained on training data to predict likely paths and allocate limited computing resources accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If limited computing resources are distributed to track all individuals in dense environments, then comprehensive surveillance coverage is improved, but resource efficiency deteriorates due to the finite quantity of hardware resources
Solution Approach 1:
The system applies different levels of surveillance intensity to different individuals based on their risk characteristics. High-risk individuals receive intensive tracking with frequent resource allocation, while low-risk individuals receive minimal or no tracking. This local differentiation of resource quality resolves the contradiction by concentrating resources where they provide maximum security value rather than uniform distribution.
Solution Approach 2:
The system dynamically changes the parameter of resource allocation intensity based on individual risk parameters. By using machine learning models to assess and update risk parameters in real-time, the system adjusts computing resource allocation accordingly, transforming the static resource distribution into a dynamic parameter-driven allocation that maintains surveillance coverage while optimizing resource efficiency.
2Measurement precision
If computing resources are concentrated on specific individuals for detailed analysis, then identification accuracy of security threats is improved, but overall surveillance coverage deteriorates due to resource constraints
Solution Approach 1:
The surveillance system segments the population into different risk groups using machine learning models. Instead of treating all individuals uniformly, the system divides them into segments such as high-risk, medium-risk, and low-risk categories. This segmentation allows concentrated resource allocation to high-risk segments for detailed analysis while maintaining broader coverage through automated monitoring of other segments, thus resolving the contradiction between precision and coverage area.
Solution Approach 2:
The system introduces machine learning risk assessment models as intermediaries between raw surveillance data and resource allocation decisions. These intermediary models process and filter information, identifying which individuals require intensive analysis and which can be monitored with standard resources. This intermediary layer enables the system to achieve high identification accuracy for threats while maintaining broad coverage through intelligent triage.
3Reliability
If forensic analysis is performed on all sensor data to identify security risks, then detection capability is improved, but processing time increases due to the volume of data
Solution Approach 1:
The system performs preliminary filtering and risk assessment on sensor data using machine learning models before conducting full forensic analysis. By pre-processing data to identify and flag only those cases that exhibit suspicious patterns or high-risk characteristics, the system eliminates the need for time-consuming detailed analysis of normal, low-risk data. This preliminary action maintains high detection capability while dramatically reducing overall processing time.
Solution Approach 2:
The system applies partial forensic analysis to the majority of low-risk individuals and excessive (full) analysis only to high-risk cases. Rather than performing complete forensic analysis uniformly on all data, the system uses risk-based triage to apply appropriate levels of analysis intensity, achieving reliable detection of security threats while minimizing processing time through selective application of computational resources.
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 a trained state of a data processing system to modify a level of surveillance of the persons. By doing so, the device management services may only be provided when certain conditions are met rather than continuously. To place the data processing system in the trained state, the data processing system may need to be trained to predict paths that individuals are likely to traverse using training data. To obtain the training data, previously traversed paths of individuals may be tracked through an environment and stored into a database.