Device Fingerprinting for Malicious Traffic Source Verification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems struggle with accurately identifying malicious network traffic, leading to false positives that can degrade network performance and cause service disruptions, while failing to effectively distinguish between legitimate and potentially harmful network activities.
Innovation Solution
A system that utilizes a bridge platform to log and analyze device characteristics, generate device fingerprints, and transmit control signals to originating client platforms to verify the authenticity of network traffic, thereby reducing false positives and enhancing network security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated systems identify potentially malicious network traffic, then network security is improved, but false positives increase causing service disruptions
Solution Approach 1:
The system performs preliminary actions by logging device characteristics and creating authorization requests before traffic is flagged as malicious. When potential threats are detected, the system checks against previously logged device fingerprints and authorization histories to verify legitimacy before taking action, preventing false positives from causing service disruptions.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring network traffic, comparing it against logged device characteristics and authorization requests, and adjusting its identification accuracy based on the results. This feedback loop enables the system to learn from previous identifications and reduce false positives over time while maintaining high security detection rates.
2Measurement precision
If device characteristics are logged and analyzed, then identification accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the identification process into distinct components: logging device characteristics, creating authorization requests with device fingerprints, and comparing potential threats against the logged data. This segmentation allows each component to be optimized independently, improving overall identification accuracy without proportionally increasing system complexity.
Solution Approach 2:
The system introduces intermediaries in the form of logged device characteristics and authorization requests that serve as reference data. These intermediaries act as mediators between the raw network traffic and the identification decision, enabling accurate identification without requiring direct complex analysis of every traffic pattern.
3Speed
If automated identification is implemented, then response speed is improved, but false positive rate increases
Solution Approach 1:
By pre-logging device characteristics and creating authorization requests before threats are detected, the system eliminates the need for complex real-time analysis when identifying potential threats. This preliminary action enables fast automated response while maintaining accuracy through comparison against established device fingerprints and authorization histories.
Data Source
AI summary
A system includes and non-transitory computer-readable media storing instructions an electronic processor configured to execute the instructions to receive a screening payload from a client platform, the screening payload identifying an authorization request, identify a stored enhanced authorization request corresponding to the identified authorization request, the stored enhanced authorization request including first device characteristics, identify a device on a network having second device characteristics, the second device characteristics matching the first device characteristics, and transmit a control signal to the identified device, the control signal configured to cause the identified device to generate an alert.