Satellite SOS Legitimacy Screening for Abuse Prevention
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Solution Overview
Problem
Existing emergency communication systems are vulnerable to abuse, such as Denial of Service (DoS) attacks and frivolous SOS requests, which overwhelm first responders and hinder effective emergency response.
Innovation Solution
Implementing Artificial Intelligence/Machine Learning (AI/ML) to analyze emergency messages for legitimacy, utilizing a history of emergency communications, device location, and other factors, and adjusting priority based on legitimacy determination, with diligence requests via application programming interfaces (APIs) to verify the authenticity of emergency communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If emergency communication systems accept all SOS requests without verification, then response time for legitimate emergencies is minimized, but the system becomes vulnerable to DoS attacks and frivolous requests that overwhelm first responders
Solution Approach 1:
The system performs preliminary analysis of emergency messages using AI/ML models before full processing, evaluating legitimacy indicators such as message characteristics, device information, and historical data to determine whether verification is needed. This preliminary action filters out obviously legitimate or obviously fraudulent messages without requiring full verification procedures.
Solution Approach 2:
The system implements feedback loops where verification results from diligence requests are analyzed and used to adjust priority assignments and routing decisions. The feedback mechanism continuously learns from verification outcomes to improve future legitimacy assessments and optimize the balance between verification thoroughness and response speed.
2Reliability
If the system implements AI/ML analysis and diligence requests for all emergency messages, then legitimacy detection accuracy improves, but system complexity and processing overhead increase
Solution Approach 1:
The system applies different levels of verification complexity to different messages based on their characteristics. Some messages receive simple legitimacy checks while others trigger comprehensive diligence requests. This local quality approach ensures thorough verification where needed while maintaining simplicity for routine messages, reducing overall system complexity.
Solution Approach 2:
The verification process is segmented into multiple independent stages: initial legitimacy assessment, selective diligence request generation, verification information collection, and final priority determination. This segmentation allows the system to apply complex AI/ML analysis only when necessary while keeping the overall process manageable and modular.
3Measurement precision
If the system processes and verifies every emergency communication thoroughly, then message legitimacy is accurately determined, but emergency services experience overload and response efficiency decreases
Solution Approach 1:
The system performs partial verification on messages that appear legitimate based on initial assessment, applying full diligence requests only when legitimacy is questionable. This partial action approach maintains high response efficiency for clear-cut cases while ensuring thorough verification when needed, optimizing the balance between accuracy and productivity.
Solution Approach 2:
The system dynamically changes verification parameters such as the depth of AI/ML analysis, the types of diligence requests issued, and the threshold for triggering additional verification based on message characteristics, device history, and current system load. This adaptive parameter adjustment maintains accuracy while preventing system overload.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, receiving, by a processing system including a processor via a satellite, an emergency communication that was transmitted from an end user device to the satellite; and determining, by the processing system, a legitimacy of the emergency communication according to an analysis based on various information such as a diligence query or challenge sent to the end user device by the satellite, a history of emergency communications associated with the end user device, a location of the end user device, a location of an emergency event associated with the emergency communication, a number of other emergency communications from other end user devices that are associated with the emergency event, a time period of the other emergency communications, locations of the other end user devices, or a combination thereof. Other embodiments are disclosed.