Aberration Engine for Unarmed Security Event Detection
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Solution Overview
Problem
Existing security systems often fail to detect abnormal activity when they are not armed, as users frequently forget to activate them, leading to missed alerts and potential security breaches.
Innovation Solution
An aberration engine that analyzes patterns of past monitoring activity to detect abnormal events, even when the system is unarmed, by assigning an 'abnormality score' to events and aggregating suspicious activities to trigger alerts or actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system remains simple and only triggers when armed, then false alarms are reduced, but security coverage is lost when unarmed
Solution Approach 1:
The monitoring system is segmented into two independent operational modes: armed mode with traditional alarm triggers and unarmed mode with aberration-based detection. The aberration engine operates as a separate analytical layer that processes sensor data differently based on the system state, allowing each mode to optimize its detection strategy without interfering with the other.
Solution Approach 2:
The system dynamically adjusts its detection behavior based on the armed/unarmed state. When unarmed, the system transitions from threshold-based triggering to pattern-based aberration detection, automatically adapting its sensitivity and analysis methodology to the current operational context without requiring manual reconfiguration.
2Reliability
If traditional threshold-based detection is used, then response time is fast, but false alarms increase during normal activity
Solution Approach 1:
The aberration engine applies partial action by not requiring a single threshold violation to trigger an alert. Instead, it accumulates multiple subtle deviations from normal patterns over time, only triggering when the aggregate aberration score exceeds the threshold. This partial detection approach reduces false alarms while maintaining timely response to genuine anomalies.
Solution Approach 2:
The system incorporates feedback loops where alert outcomes and user responses are fed back into the pattern learning mechanism. When users dismiss false alarms or confirm genuine threats, this feedback refines the aberration calculations and pattern recognition, progressively improving alert accuracy and reducing future false positives.
3Reliability
If the system monitors all events continuously, then detection capability is enhanced, but energy consumption increases
Solution Approach 1:
The aberration engine employs periodic analysis intervals rather than continuous processing. It collects sensor events and performs aberration analysis at scheduled intervals, allowing the system to remain in a lower-power state between analysis cycles while still maintaining effective monitoring coverage through the accumulated event data.
Data Source
AI summary
An aberration engine that collects data sensed by a monitoring system that monitors a property of a user and aggregates the collected data over a period of a time. The aberration engine detects, within the aggregated data, patterns of recurring events and, based on detecting the patterns of recurring events within the aggregated data, takes action related to the monitoring system based on the detected patterns of recurring events within the aggregated data.