Distributed Aimbot Detection via Trusted Execution Environment
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Solution Overview
Problem
Current aimbot detection methods face challenges such as high costs, scalability issues, and ease of bypass, particularly in online first-person shooter games.
Innovation Solution
A distributed aimbot detection system utilizing a trusted execution environment (TEE) and a deep learning model is implemented, which receives game play information from a game server, transforms the data, and uses an aimbot detection model to predict player actions, determining if an aimbot is being used.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a central server monitors all player actions in real-time, then detection accuracy is improved, but server cost and scalability deteriorate
Solution Approach 1:
The detection system is segmented between client and server. The client performs local action data transformation and preliminary processing, while the server only receives transformed data and performs final detection. This segmentation reduces server workload and improves scalability while maintaining detection accuracy through distributed computation.
Solution Approach 2:
The patent extracts the action data transformation function from the server and places it in the client. The client extracts relevant features and transforms action data locally before sending to the server, reducing the amount of data transmitted and processed by the server, thus lowering server cost and improving scalability.
2Device complexity
If statistical value-based detection is used, then server computation is reduced, but detection accuracy deteriorates
Solution Approach 1:
The patent introduces an intermediary transformation layer at the client side that converts raw action data into transformed action data with extracted features. This intermediary layer prepares data in a format suitable for server processing, enabling the server to perform accurate detection with reduced computation load by working with pre-processed features rather than raw data.
3Ease of manufacture
If traditional aimbot detection methods are used, then implementation is simple, but ease of bypass increases
Solution Approach 1:
The patent changes the detection parameters from simple statistical values to transformed action data that includes extracted features and temporal information. By transforming the data representation and using multiple features (position, angle, time intervals), the system becomes more difficult to bypass while remaining implementable through standardized deep learning frameworks.
Data Source
AI summary
Disclosed is a distributed aimbot detection system and method using a trusted execution environment (TEE) and deep learning. An aimbot detection system may include a data management module configured to receive game play information including action data of a game player from a game server; and an aimbot detection module configured to derive aimbot detection results from the action data included in the received game play information using an aimbot detection model, and the data management module and the aimbot detection module may be configured in a trusted execution environment (TEE) of a client.