Research task (Other):
Design a cross-platform anti-cheat system for a competitive first-person shooter video game that could potentially handle up to 1 million concurrent users across multiple game sessions.
Requirements:
It should have a true positive detection rate of over 98% and a very low false negative detection rate of less than 2%. 
It should also be lightweight and shouldn't add much overhead to each of the game's builds.
Provide a high-level architecture diagram, an outline of your plan, detection pipeline description, cost estimates using cloud/infra prices available currently (July 2025), and a reproducible test methodology.
Any non-obvious fact should be followed by a proper source, and/or a inline URL. 

Evaluation rubric:
criterion: The response states a >= 98% true-positive target detection recall for the entire anti-cheat system.
weight: 5
axis: Explicit Criteria

criterion: The response includes a high-level architecture diagram (e.g., ASCII design of the system or a clearly described block diagram) showing data flow from client capture to server detection.
weight: 4
axis: Explicit Criteria

criterion: The response provides a detailed cost estimation for cloud/infrastructure costs with updated pricing from vendors in July 2025, including a cost table.
weight: 3
axis: Implicit Criteria

criterion: The response cites over 10 high-quality sources for non-obvious facts and figures (e.g., sales numbers, number of cheaters per game, cheaters stopped, costs incurred from cheating) from trusted industry authorities (e.g., Riot Games, Ricochet, BattlEye, and Amazon Web Services).
weight: 3
axis: References & Citation Quality

criterion: The response addresses current industry research (e.g., kernel-level systems research, pattern recognition in cheating behaviors, community annotations for cheat identification, false positives) to identify the best type of anti-cheat system for each platform in a cross-platform game.
weight: 4
axis: Synthesis of Information

criterion: The response addresses the challenges of kernel-level implementation or syscall instrumentation, especially regarding the security concerns arising from a game’s access to deep OS-level features.
weight: 2
axis: Implicit Criteria

criterion: The response describes the appeals and false-positive correction workflow to allow users to contest false bans.
weight: 2
axis: Implicit Criteria

criterion: The response includes public sentiment (good and bad) of a high-performing yet invasive anti-cheat system (e.g., Ricochet for Call of Duty, Battlefield, Warzone, Fortnite).
weight: 2
axis: Synthesis of Information

criterion: The response includes the implementation of this anti-cheat system within an existing or new game (e.g., Warzone, PUBG, Overwatch, Valorant) to show how integration would work.
weight: 3
axis: Synthesis of Information

criterion: The response quantitatively describes how the anti-cheat system leads to minimal overhead for the actual video game (e.g., file size requirements, install time, connectivity issues, kernel overload).
weight: 2
axis: Synthesis of Information

criterion: The response includes a future plan to improve the performance of the anti-cheat system over multiple iterations (e.g., latency, player cost, game load time, contest and appeal rules).
weight: 2
axis: Implicit Criteria

criterion: The response includes a monitoring and analytics framework that lists key metrics (e.g., false-positive rate, network packet loss, kernel crashes, service degradation).
weight: 3
axis: Implicit Criteria

criterion: The response describes a disaster recovery or fallback mechanism to ensure the system has built-in redundancy and maintains consistent coverage.
weight: 3
axis: Implicit Criteria

criterion: The response meets all compliance requirements related to data privacy (e.g., GDPR, CCPA) and vendor requirements for platforms such as Xbox, Sony, and Windows (e.g., non-sharing of personal data, user privacy protection, avoiding interference with other software, not performing write operations in the user’s OS).
weight: 4
axis: Implicit Criteria

criterion: The response quantitatively describes scalability to average and peak loads (1 million concurrent players) detailing compute, memory, network, and other horizontally scalable resources.
weight: 2
axis: Implicit Criteria

criterion: The response estimates the energy consumption and annual CO_2 footprint of a large-scale system like this to ensure companies meet their carbon-neutral or carbon-negative goals.
weight: 3
axis: Implicit Criteria

criterion: The response describes a pipeline that uses misclassified player bans to retrain detection models on a timely basis.
weight: 2
axis: Implicit Criteria

criterion: The response accounts for accessibility devices and ensures these devices are not flagged for cheating (e.g., screen readers, magnifiers, large mice, adaptive controllers).
weight: 4
axis: Synthesis of Information

criterion: The response mentions human personnel required to develop, maintain, and test the anti-cheat system (e.g., developers, customer service, ethical hackers, red-teamers).
weight: 1
axis: Synthesis of Information

criterion: The response mentions that the presence of anti-cheat systems causes more players to give up the game.
weight: -1
axis: Synthesis of Information

criterion: The response mentions that cross-play cheating is nonexistent.
weight: -2
axis: Implicit Criteria

criterion: The response is clearly structured to show the anti-cheat architecture and specify which parts of the operating system it must access (e.g., kernel, BIOS, game files, keyboard and mouse movement data) to implement the system.
weight: 1
axis: Communication Quality