Aberration Engine for Unarmed Security Event Detection
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Solution Overview
Problem
Conventional security systems fail to detect abnormal activity effectively when not armed, especially when users forget to activate them, leading to potential security breaches.
Innovation Solution
An aberration engine that monitors events and assigns abnormality scores, aggregating suspicious events over time to alert users and potentially trigger alarms even when the system is unarmed, using patterns of recurring activities to determine normal versus abnormal behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the alarm system is not armed to avoid false alarms and maintain ease of use, then the system remains simple to operate, but security detection capability deteriorates
Solution Approach 1:
The system performs preliminary actions by continuously monitoring events and learning normal patterns in advance, even when unarmed. This allows the system to be ready to detect abnormalities without requiring the user to arm the system, resolving the contradiction between ease of operation and security detection capability
Solution Approach 2:
The aberration engine performs self-service by automatically learning normal event patterns and autonomously detecting abnormalities without user intervention or system arming. The system serves itself by maintaining security monitoring capabilities while remaining easy to operate
2Reliability
If the alarm system continuously monitors all events to detect abnormalities, then security detection capability improves, but energy consumption and processing load increase
Solution Approach 1:
The system performs preliminary learning of normal patterns during low-activity periods, so that during active monitoring it can quickly compare events against established patterns without heavy processing. This resolves the contradiction by preparing detection criteria in advance, reducing real-time energy consumption while maintaining high security detection capability
Solution Approach 2:
The system uses periodic action by learning patterns over time periods and updating abnormality scores at intervals rather than continuously processing all events with full analysis. This reduces energy consumption while maintaining effective security detection through periodic pattern matching and score aggregation
3Measurement precision
If the system aggregates multiple suspicious events to trigger alerts, then measurement precision of abnormal activity improves, but response time delays increase
Solution Approach 1:
The system applies dynamics by adjusting the abnormality score threshold and aggregation requirements based on the situation. When a single event reaches a high abnormality score, the system can trigger an alert immediately without requiring multiple events, thus maintaining measurement precision while reducing response time for critical threats
Solution Approach 2:
The system changes parameters dynamically by adjusting the number of events required for aggregation and the weight of individual events based on their abnormality scores. This allows the system to maintain high measurement precision for complex patterns while enabling faster response when parameter changes indicate critical abnormalities
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.