Edge Network Malicious Device Identification via Crowdsourced Fingerprinting
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Solution Overview
Problem
In edge computing networks, particularly in 5G/B5G verticals like intelligent transportation systems and industrial IoT, identifying malicious users such as jammers is challenging due to their ability to manipulate location information, leading to vulnerabilities in network security and potential attacks like jamming and misbehavior.
Innovation Solution
The system employs crowdsourcing to establish location-based fingerprinting regions at the edge infrastructure, using wireless channel properties to create unique fingerprinting regions and map received signals from potentially malicious users, thereby identifying their location through a neural network-based approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If compute resources are placed in remote locations (edge computing), then network latency is reduced and service capabilities are improved, but security clearance becomes unknown and security level fluctuates
Solution Approach 1:
The system performs preliminary location verification by establishing fingerprinting regions and mapping device locations before security-critical operations. This advance preparation allows the system to assess security clearance and make authorization decisions based on predetermined geographic boundaries and device location accuracy, rather than reacting to security concerns after deployment.
Solution Approach 2:
The patent introduces location verification and fingerprinting region mapping as an intermediary mechanism between remote compute resources and the network. This intermediary layer provides a way to assess and verify device locations and security clearances, acting as a mediator that enables secure access to edge computing resources without requiring fixed physical locations.
2Reliability
If device locations are verified using traditional methods, then security clearance can be determined, but malicious users can manipulate location information to bypass verification
Solution Approach 1:
The system transitions from verifying location information in a single dimension (trusted/not trusted) to multiple dimensions by creating geographic fingerprinting regions with varying levels of trust. Devices are mapped to specific regions with different security clearances based on their location accuracy and regional trust levels, adding spatial granularity and multiple verification layers that make manipulation more difficult.
Solution Approach 2:
The patent changes the parameter of location verification from binary (verified/unverified) to a spectrum of trust levels based on fingerprinting region mapping. By introducing parameters such as location accuracy, regional trust scores, and dynamic region boundaries, the system creates a more nuanced verification mechanism that adapts to different security contexts and makes static manipulation attacks less effective.
3Measurement precision
If fingerprinting regions are established using crowdsourced locations, then location accuracy is improved, but system complexity increases due to neural network processing requirements
Solution Approach 1:
The system segments the verification process into distinct components: crowdsourced location collection, fingerprinting region generation, device location mapping, and security clearance determination. This segmentation allows each component to be optimized independently and processed in parallel, reducing the computational burden on any single system and enabling distributed implementation across multiple edge nodes.
Solution Approach 2:
The patent implements partial verification by mapping devices to fingerprinting regions with different levels of detail and trust. Not all devices require the same level of verification - the system performs appropriate verification based on device type, service requirements, and regional trust levels. This partial action approach reduces overall system complexity while maintaining adequate security for critical operations.
Data Source
AI summary
Various aspects of methods, systems, and use cases include region identification of a malicious device based on crowdsourced locations. A method may include generating a grid of bins of a local radio coverage region, receiving location data from a plurality of devices in the local radio coverage region, and classifying locations of the plurality of devices with respect to the bins. The method may include associating the classified locations of the plurality of devices to the received location data for corresponding devices of the plurality of devices, and generating a model, from the associated classified locations and the received location data.