Location-Based Behavioral Monitoring for Security Detection
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Solution Overview
Problem
Existing location-based monitoring systems fail to prevent tailgating and detect abnormal behavioral variations, as they do not effectively track and analyze the movements of individuals in real-time, leading to security vulnerabilities.
Innovation Solution
A location-based behavioral monitoring system that stores access lists, historical location parameters, and schedule parameters to predict future locations and determine risk ratings, using methods such as location tracking, access requests, and facial recognition, with the ability to activate alarms for high risk ratings and display risk ratings on maps or timelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional access control devices are used to restrict access to certain locations, then access control functionality is provided, but the system fails to detect abnormal behavioral variations and prevent tailgating
Solution Approach 1:
The patent combines multiple monitoring functions into a unified location-based behavioral monitoring system. The system integrates access control data, location tracking, historical behavior analysis, and real-time anomaly detection into a single comprehensive platform, allowing it to detect tailgating and abnormal behaviors while maintaining manageable system complexity through centralized architecture.
Solution Approach 2:
The behavioral monitoring system serves multiple security functions simultaneously: it monitors location parameters, analyzes historical behaviors, predicts expected behaviors, detects anomalies in real-time, and provides alerts for incidents like tailgating. This multi-functional approach enhances security detection capability without requiring separate specialized systems for each function.
2Measurement precision
If location-based tracking and behavioral analysis are implemented, then abnormal behavior detection capability is improved, but data processing requirements and system resources increase
Solution Approach 1:
The system extracts only the essential location parameters and behavioral indicators needed for anomaly detection from the vast amount of generated data. By focusing on key metrics such as location coordinates, timestamp, access control events, and pre-defined behavioral patterns, the system achieves high behavioral analysis accuracy while minimizing the actual data processing load required.
Solution Approach 2:
The system processes location and behavioral data at appropriate levels of detail without unnecessary overhead. It collects precise location information and behavioral events but processes them using efficient algorithms that analyze only the necessary aspects of the data, avoiding excessive computation while maintaining high detection accuracy through targeted analysis.
3Speed
If real-time location parameter collection and risk rating determination are performed, then security response time is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-collecting and organizing location parameters, historical behavior data, and access control information before real-time monitoring is needed. It pre-establishes baseline behavioral patterns and criteria for anomaly detection, so that when real-time location data arrives, the system can quickly compare it against pre-prepared reference data and generate risk ratings without intensive real-time computation.
Solution Approach 2:
The system implements feedback mechanisms where risk ratings and anomaly detections are continuously updated based on incoming location parameters and comparative analysis against historical data. This feedback loop allows the system to maintain high security response speed by using previously learned patterns and adjusting risk assessments in real-time based on current behavioral deviations, rather than performing complete re-analysis of all data.
Data Source
AI summary
A method of location-behavioral monitoring is provided. The method comprising: storing an access list associated with an identification credential; collecting historical location parameters associated with the identification credential; receiving schedule parameters associated with the identification credential; determining a predicted location schedule associated with the identification credential in response to the historical location parameters and schedule parameters; collecting location parameters associated with identification credential; and determining a risk rating associated with each location parameter in response to the predicted location schedule, location parameters, and the access list.