Aberration Engine for Unarmed Security Anomaly Detection
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Solution Overview
Problem
Conventional security systems fail to detect abnormal activity when not armed, leading to missed alerts and increased false alarms, as they rely on user input to define alert conditions.
Innovation Solution
An aberration engine that analyzes patterns of past monitoring activity, even when the system is unarmed, to detect anomalies and send alerts without user-defined rules, using a scoring system to determine the abnormality of events and adjust alert responses accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional security systems rely on user-defined rules and armed state to detect events, then the system operation is simple, but the system fails to detect abnormal activity when unarmed leading to missed alerts
Solution Approach 1:
The system performs self-learning by automatically analyzing historical sensor data to establish baseline patterns of normal activity. The aberration engine autonomously identifies deviations from these patterns without requiring user intervention or predefined rules, enabling the system to detect abnormalities even when unarmed.
Solution Approach 2:
The system pre-processes and stores historical sensor data during normal operation to build a comprehensive understanding of typical activity patterns. This preliminary analysis enables the system to quickly identify anomalies when they occur, rather than requiring users to manually configure detection parameters in advance.
2Reliability
If the system monitors all activity continuously, then detection coverage is improved, but false alarms increase
Solution Approach 1:
The system dynamically adjusts detection sensitivity by comparing current activity against learned baseline patterns. Rather than using fixed thresholds, the aberration engine evaluates deviations in multiple parameters simultaneously (timing, sequence, context) to determine whether an event represents a true anomaly or normal variation, reducing false alarms while maintaining comprehensive coverage.
Solution Approach 2:
The system continuously refines its understanding of normal patterns by incorporating feedback from ongoing monitoring. When users confirm or dismiss alerts, this information feeds back into the learning process, allowing the system to adjust its detection criteria and reduce false alarms over time while maintaining high detection coverage.
3Ease of operation
If user-defined rules are used to control alerts, then the system is easy to operate, but the system cannot detect abnormal activity without predefined conditions
Solution Approach 1:
The system automatically learns and adapts to each user's specific patterns of normal activity through continuous analysis of historical data. This self-service approach eliminates the need for users to manually define detection rules while providing highly customized and accurate anomaly detection tailored to their specific environment and behavior patterns.
Solution Approach 2:
The detection system transitions from static, user-defined rules to dynamic, adaptive pattern recognition. The aberration engine continuously evolves its understanding of normal activity based on changing patterns in the monitored environment, allowing the system to adapt to seasonal variations, changes in user behavior, and new types of abnormalities without requiring manual reconfiguration.
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.