sUAS Defense via Wireless Packet Analysis and Exploit Injection
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Solution Overview
Problem
The growing use of small unmanned aerial systems (sUAS) for commercial purposes poses safety and privacy concerns due to unaddressed regulatory and sense-and-avoid technology complexities, necessitating a method to control and defend against these systems.
Innovation Solution
A system and method that detects a wireless access point associated with a sUAS, analyzes data packets using a machine learning classifier to determine the sUAS type, and transmits exploits to initiate control or defensive actions, including interrupting communication or causing the sUAS to crash, using non-intrusive or intrusive exploits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sUAS are used for widespread commercial purposes, then productivity and commercial utility are improved, but safety and privacy risks increase
Solution Approach 1:
The system performs preliminary detection and classification of sUAS threats before they can cause harm. By using machine learning classifiers to identify sUAS types and potential threats in advance, the system can prepare and execute appropriate countermeasures proactively, preventing safety incidents before they occur while allowing legitimate commercial operations to continue
Solution Approach 2:
The patent introduces an intermediary defense system that operates between legitimate sUAS operations and potential threats. This intermediary system uses wireless communication interception and analysis to mediate between commercial sUAS usage and safety concerns, enabling control or disruption of threatening sUAS without affecting normal commercial operations
2Measurement precision
If machine learning classifiers are used to identify sUAS types, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system creates simplified copies or representations of complex sUAS identification problems by using machine learning classifiers that process wireless communication patterns. Instead of directly analyzing complex physical characteristics of sUAS, the system copies communication data patterns and uses classifiers to identify sUAS types, reducing the complexity of direct detection while maintaining identification accuracy
3Reliability
If exploits are transmitted to control or disrupt sUAS, then defensive capability is improved, but risk of unauthorized control increases
Solution Approach 1:
The system implements feedback mechanisms where the effects of transmitted exploits are monitored and analyzed. By observing the responses from sUAS after exploit transmission, the system can determine whether the intended defensive effect was achieved and adjust subsequent actions accordingly, ensuring that defensive measures remain controlled and effective without causing unintended unauthorized control
Data Source
AI summary
Provided is a method and a computer device for performing the method for defending a perimeter against a small unmanned aerial system (sUAS). The method includes detecting a presence of a wireless access point (WAP) associated with a sUAS; analyzing data packets intercepted from the WAP; determining the type of sUAS based on the data packets that were intercepted using a machine learning classifier; determining one or more exploits from a library of exploits to initiate against the sUAS based on the type of sUAS determined by the machine learning classifier; and transmitting the one or more exploits to the sUAS.