Trusted User Verification Using Device Interaction History
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Solution Overview
Problem
Existing identity verification methods for electronic devices are ineffective against advanced bots, which can mimic human behavior and compromise online services, leading to issues like DDoS attacks and inconvenience for human users.
Innovation Solution
The system utilizes SSH key pairs, UUIDs, biometric sensors, and various interaction data to verify a user's identity by generating unique identifiers (UUIDs) based on device interactions and human-like behaviors, ensuring the device has not been compromised, and requiring a history of trusted interactions to confirm user authenticity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If CAPTCHA programs are used to verify human users, then bot access is limited, but advanced bots can solve these queries and the check becomes ineffective
Solution Approach 1:
The verification process is segmented into multiple independent checks: device identifier verification, interaction history analysis, behavioral pattern recognition, and challenge-response validation. Each segment evaluates a different aspect of user authenticity, making it harder for bots to compromise the entire system by mimicking only one behavior pattern.
Solution Approach 2:
The system performs preliminary verification by analyzing device identifiers and interaction histories before presenting challenges to users. This preliminary action establishes a baseline of trustworthiness, allowing the system to differentiate between new and established users and apply appropriate verification levels.
2Productivity
If time-based challenges are implemented to limit bot requests, then bot flooding is reduced, but human users experience inconvenient delays
Solution Approach 1:
The verification process dynamically adapts to user context by adjusting challenge requirements based on device identifier reputation, interaction history, and behavioral patterns. Trusted devices with clean histories experience minimal or no delays, while suspicious devices undergo more rigorous verification, optimizing both security and user convenience.
Solution Approach 2:
The system changes verification parameters such as challenge type, number of required checks, and time thresholds based on the assessed risk level of each user session. This allows the system to maintain high security for unknown devices while providing fast access for verified trusted users.
3Reliability
If blacklists of known bots are maintained, then malicious user access is blocked, but bots can avoid detection by pretending to be new users or mimicking human behavior
Solution Approach 1:
The verification system adds new dimensions of analysis beyond simple blacklist matching, including device identifier verification, interaction history depth, behavioral pattern consistency, and cross-service reputation. This multi-dimensional approach prevents bots from evading detection by merely changing their identity or mimicking human behavior in isolated contexts.
Solution Approach 2:
The system implements continuous feedback loops where verification results from one interaction update the device's reputation score and trust level. This feedback mechanism allows the system to adapt to emerging bot strategies by learning from failed verification attempts and adjusting future verification requirements accordingly.
4Ease of operation
If IP addresses are used to identify trusted users, then service access can be streamlined, but IP addresses can be duplicated by computers and bots can gain access
Solution Approach 1:
The system uses asymmetric verification where the burden of proof is distributed differently based on trust level. Trusted devices with established histories experience streamlined verification, while new or suspicious devices undergo more rigorous checks. This asymmetric approach optimizes both convenience and security by adapting verification intensity to individual device reputations.
Solution Approach 2:
The system introduces device identifiers and interaction histories as intermediary verification elements between the user and the service. These intermediaries provide additional layers of authentication that go beyond simple IP address matching, enabling the system to verify user identity more reliably while maintaining fast access for trusted users.
Data Source
AI summary
The following invention is directed towards user identity verification and utilizes enhanced abilities to detect both a user's identity and confirming that the user is in fact a person and not a bot. The device utilizes Universal Unique Identifiers (UUIDs) indicating the device's interaction with the various environmental and electronic devices located around it. These interactions can indicate if a user has human characteristics. As a user interacts with their device and that device interacts with the surrounding world, a UUID is created and is then stored upon a user's device as well as created in an online database. When a user wishes to access an online service, for example, social media, the user will make a request to that service. The online service may then ask for a list of UUIDs from the user device to verify that they are who they say they are. If these UUIDs satisfy the request, the user identity is confirmed.