Spoofing Detection via Computational Challenges
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Solution Overview
Problem
Online security systems face challenges in detecting and preventing sophisticated attacks, such as application program spoofing, device type spoofing, and session token spoofing, especially in high-volume digital interactions, where existing methods are ineffective in verifying the authenticity of user identities and device types.
Innovation Solution
Implementing techniques that involve interacting with client devices to verify the authenticity of application programs and device types through computational challenges and session token management, including serial cumulative calculation challenges, parallel checkpoints calculation challenges, and neural network calculation challenges, to ensure that communications are legitimate and come from authorized devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computational challenges are implemented to verify device authenticity, then security against spoofing attacks is improved, but processing time and system complexity increase
Solution Approach 1:
The system performs computational challenges as preliminary verification steps before granting access to digital interactions. By requiring devices to complete calculation challenges (serial cumulative, parallel checkpoints, or neural network calculations) before processing main transactions, the system verifies device authenticity in advance, preventing spoofing attacks while maintaining real-time processing capability for legitimate users.
Solution Approach 2:
The verification process is divided into multiple independent challenge types (serial cumulative calculation, parallel checkpoints calculation, neural network calculation) that can be selectively applied. Each challenge type segments the verification task into manageable computational steps, allowing the system to distribute processing load and optimize based on device capabilities and security requirements.
2Measurement precision
If multiple computational challenge types are used to detect spoofing, then detection accuracy is improved, but device complexity and processing overhead increase
Solution Approach 1:
Different computational challenge types are applied selectively based on local conditions such as device capabilities, security risk levels, and interaction types. The system chooses specific challenge types (serial cumulative for basic verification, parallel checkpoints for medium complexity, neural network for high security requirements) rather than applying all challenges uniformly, thereby optimizing detection accuracy while minimizing unnecessary processing overhead.
3Reliability
If real-time verification of device authenticity is performed, then security against spoofing is improved, but processing speed for high-volume interactions decreases
Solution Approach 1:
The verification system dynamically adjusts the type and complexity of computational challenges based on real-time assessment of device behavior, interaction patterns, and security risk indicators. For low-risk interactions, simpler verification methods are used to maintain high processing speed; for suspicious or high-value interactions, more rigorous computational challenges are applied, balancing security requirements with processing throughput for high-volume digital interactions.
Data Source
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AI summary
Techniques for detecting application program spoofing. The techniques include: receiving a communication from an application program executing on a client device different from the at least one computer; identifying from the communication an asserted identity of the application program; and verifying the asserted identity of the application program at least in part by: interacting with the client device to obtain additional information about the application program, and determining whether the additional information about the application program is consistent with the asserted identity of the application program.