Security System Device Metadata Matching for Breach Detection
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Solution Overview
Problem
Current security systems in retail environments are unable to accurately track or identify devices associated with individuals who have previously stolen items, leading to high false positive notifications and inaccurate profiling.
Innovation Solution
A method and apparatus for controlling a security system by receiving device metadata from devices in communication with a wireless access point, matching it with historical data associated with security breach events, and generating an alert if the metadata corresponds with a security breach probability value exceeding a threshold, thereby initiating security functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing security systems use preexisting customer identifiers for profiling, then security monitoring can be performed, but the system becomes highly inaccurate with high false positive notifications
Solution Approach 1:
The system performs preliminary actions by collecting and storing device metadata (MAC addresses, device types, locations) before security breaches occur. This historical data is used to build profiles of devices associated with past breach events, enabling more accurate future identification without relying on imprecise preexisting customer identifiers.
Solution Approach 2:
The system uses feedback from historical security breach events to refine future identification accuracy. By comparing current device metadata against historical data from confirmed breach events, the system continuously improves its ability to distinguish actual threats from false positives, creating a feedback loop that enhances measurement precision.
2Measurement precision
If the system tracks and identifies devices associated with previous thefts, then security breach detection improves, but device complexity increases
Solution Approach 1:
The system extracts only the necessary metadata elements (MAC address, device type, location) from devices and stores them in a historical database. By focusing on these specific extractable features rather than attempting to track all device information, the system achieves improved detection accuracy while keeping the complexity manageable.
Solution Approach 2:
The system creates a simplified copy of device identification through metadata profiles stored in the historical database. Instead of tracking complete device states, the system uses these metadata copies to identify patterns associated with past breaches, reducing the complexity of ongoing tracking while maintaining detection precision.
3Measurement precision
If the system uses historical device metadata matching, then false positive notifications are reduced, but the database storage requirements increase
Solution Approach 1:
The system extracts only essential device metadata fields (MAC address, device type, location) for storage in the historical database. By storing only these critical identifying features rather than complete device information, the system minimizes storage requirements while maintaining sufficient data for accurate matching and false positive reduction.
Solution Approach 2:
The system applies different storage strategies to different data elements based on their importance. Critical identification fields are stored with high precision for matching, while less important data is either omitted or stored in condensed formats, optimizing the balance between storage volume and detection accuracy.
Data Source
AI summary
Example implementations include a method, apparatus and computer-readable medium of controlling a security system by a computer device, comprising receiving device metadata associated with a device in communication with a wireless communication access point. The implementations further include determining that the device metadata comprises matched device metadata based on a match with one of a plurality of previously-identified device metadata in a historical database that stores the plurality of previously-identified device metadata in association with one or more of a plurality of security breach events and a corresponding one of a plurality of security breach probability values. Additionally, the implementations further include determining that the matched device metadata corresponds with a security breach probability value that exceeds a security breach probability threshold. Additionally, the implementations further include generating an alert and sending the alert to a security system device, wherein the alert is configured to initiate a security function.