Mitigating in-game cheating: An overview of modern anti-cheat strategies
If you’re part of the gaming industry, you’re likely well aware of the pervasive problem of in-game cheating, ranging from hacked accounts in online multiplayer games to exploits in single-player experiences. Studies suggest that nearly 70% of gamers have encountered cheating and inauthentic behavior, leading to a decline in player trust, engagement, and ultimately, revenue. The loss can be roughly sliced: The loss of revenue from in-game stores due to IAP cheating, trading digital assets that were acquired fraudulently, and weakened player retention.
Developers and publishers suffer from reputational damage, direct and indirect revenue losses, increased churn, and more, due to the continuous surge of online game cheating.
To combat this growing threat, the industry invests in ever-evolving anti-cheat strategies, such as advanced detection algorithms, stricter penalties, and community-driven initiatives. The battle against cheating is a constant arms race, demanding continuous innovation and adaptation to keep pace with the evolving tactics of cheaters.
Cheating types plaguing online Games – A few examples:
- Automation: Scripts that automate gameplay, giving players unfair advantages such as enhanced skill level, higher win rate, or more resources by playing non-stop for longer periods.
- Farming: Exploiting automated gameplay to gain resources or experience bypasses the intended gameplay loop (e.g., Elden ring).
- Emulators: Running games on unauthorized platforms can potentially grant unfair advantages (e.g., Call of Duty).
- Hot Keys: Instant execution of complex actions removes the need for manual skill
- Input Devices/Modifiers: Rapid-fire controllers bypass human limits, offering impossible accuracy (e.g., Destini-2).
- Wintrading: Manipulating the matchmaking system for easy wins, often for personal gain (e.g., wintrading rings in League of Legends).
Additional cheating types are related to commercial cheating (vs. indie cheating):
– Account selling
– Grey market (selling assets outside of the game and trading them in-game)
– Account takeover
– API attack/backend spoofing
How wide is the cheating phenomenon?
Surveys conducted in recent years indicate that nearly 77% of players have encountered cheaters during their gaming sessions, leading to frustration and jeopardizing the integrity of the gaming experience.
Of the online gamers, only 12% have never had their multiplayer gaming experience negatively impacted by other players’ inauthentic behavior. In other words, about 9 in 10 players have had a negative experience because of cheaters.
Not surprisingly, 76% of respondents also stated that it is important for them that multiplayer games online are secured against players with unfair advantages. Source
Moreover, it is estimated that cheating costs the industry up to $29 Billion!
Traditional anti-cheat strategies: strengths and weaknesses
Pattern-based detection identifies known cheating patterns and behaviors. This approach is similar to how antivirus software works. It relies on pre-defined patterns of known cheats to flag suspicious activity.
- Effective against known cheats.
- Relatively simple to implement.
- Reactive approach, unable to detect new cheats until they are identified and patterns are created.
- Vulnerable to modifications of existing cheats, requiring constant updates and pattern maintenance.
- Event Specificity: Limited to detecting cheats similar to the ones it was trained on.
Anomaly-based detection analyzes player behavior and game data to identify suspicious patterns that deviate from normal gameplay (Anomalies). This approach relies on algorithms that learn from known cheats and attempt to generalize to identify new ones.
- Can detect new cheats based on suspicious behavior patterns.
- More adaptable than pattern-based detection.
- Can be prone to false positives, flagging legitimate players as cheaters.
- Requires careful tuning to avoid false positives and false negatives. Hence it’s time-consuming and expensive.
- Constant Adaptation: Requires constant updates to keep up with evolving cheats.
- inability to detect cheats that closely mimic human behavior, as these cheats do not create anomalies. One example of such cheats is the “reply attack.”
Server-side validation involves running game logic on the server side to verify the accuracy of player actions and game state. This approach aims to prevent the manipulation of in-game data by cheaters (also called moding).
- Provides a high level of security as game logic is not exposed to client-side manipulation.
- Can detect some sophisticated cheats that modify game data.
- High resource consumption on servers.
- Can introduce lag and performance issues for players.
- Effective against specific types of cheats, particularly those that exploit server-side vulnerabilities.
Summarizing the Limitations of Traditional Anti-Cheat Strategies
While traditional anti-cheat methods play a crucial role in preventing cheating, they face significant limitations:
- Reliance solely on limited pre-defined game events – This creates huge blindspots as it looks only at what actions that players took without the nuances that show how they take these actions- what strategies are they using (which is a key in cheating)
- Reactive Nature: These methods require identification and response to new cheating methods as they emerge, leaving a window of vulnerability for cheaters to exploit.
- Inconsistency in Effectiveness: The effectiveness of these methods can vary depending on the type of cheat and the skill of the cheater. Sophisticated cheaters may be easily able to bypass traditional detection methods.
- High Resource Consumption: Server-side validation and manual moderation require significant resources from game developers and publishers, potentially impacting performance and scalability.
These limitations highlight the need for more advanced and proactive anti-cheat solutions to combat the evolving landscape of cheating in online games.
ML-based anti-cheat strategy with sensor-powered justice
As mentioned, traditional anti-cheat methods offer valuable tools, but their blindspots leave room for improvement. A new emerging approach is leveraging the power of ML and mobile device sensor data signals to combat cheating more effectively.
Sensor-based game analytics
Gaming devices come equipped with sensors like touch metrics, accelerometer, gyroscope, and more, tracking user interactions and movements. These sensors generate a unique dataset, offering insights into not just what players do, but how they do it with unprecedented granularity. Analyzed with the right tools, this exclusive dataset offers valuable insights into player behavior, uncovering hidden opportunities for anti-cheat detection.
Machine learning algorithms analyze sensor data to identify in-game anomalies and recognize patterns that deviate from typical gameplay in real-time. This allows for the detection of suspicious behavior even if the specific cheat being used is unknown. There are plenty of products able to detect cheaters, but an ML-based solution learns from players and adapts itself to fit the specific game. Moreover, as it keeps being trained on the data, it is always kept up to date with no need for manual maintenance when new versions of the game are released or new cheats are deployed by the cheaters.
Most importantly, an ML-based solution that leverages device sensor signals allows an unprecedented understanding of human behavior, thereby providing a new level of accuracy in detecting unauthentic player behavior, including cheating. This enhanced accuracy makes its cheat detection much harder to fool.
Unlike traditional methods that react to known cheats or are limited to pre-defined tracked events, AI-based anti-cheat solutions offer a proactive approach, simultaneously analyzing huge chunks of data signals.
Beyond the obvious: sensor-based analytics unsung advantages
Leveraging mobile device sensor data in conjunction with AI offers unique advantages for anti-cheat solutions:
- Unveiling Hidden Anomalies: Sensor data captures subtle movements and interactions uncovering intricate patterns that traditional methods can’t detect, hence enabling early identification of potential cheaters.
For instance, traditional methods struggle to detect automation scripts like rapid-fire controllers, which bypass human limitations and offer impossible accuracy. These scripts automate key presses and actions, simulating superhuman reflexes and aiming precision. However, sensor-based AI can analyze subtle variations in player input, such as touch durations and micro-movements, revealing patterns indicative of automated behavior. This allows for the detection of previously undetectable cheats, ensuring a fairer and more competitive environment for all.
- Adaptability to Evolving Tactics and Continuous Learning: Machine learning algorithms trained to analyze human behavior in diverse sensor data can adapt and evolve to recognize new cheating techniques, providing a more robust and future-proof solution. As cheaters are constantly innovating, devising new ways to exploit game mechanics and gain an unfair advantage, this benefit is crucial.
For example, imagine the emergence of a new hotkey script that allows players to instantly execute complex actions in a game similar to League of Legends. While traditional methods might not initially recognize this new form of cheating, sensor-based AI can detect subtle changes in touch patterns, such as the duration and frequency of touches, which can reveal the use of hotkey scripts. Alternatively, it can measure the time it takes for a player to react to in-game stimuli, which can help to identify automated scripts that provide superhuman reflexes.
In our hotkey script example, sensor-based AI would be able to detect the script by analyzing the changes in the player’s touch pattern. As the player presses the hotkey and the script executes the complex action, the AI observes a sudden change in the timing and sequence of touches. This deviation from normal behavior would be flagged as suspicious and could be investigated further.
Efficiency through Automation: Sensor-based AI automates cheat detection, reducing the need for manual reviews and freeing up resources for game development.
To illustrate this, let’s compare the identification processes:
Traditional anti-cheat example:
- A player reports suspicious activity to the game developer.
- The developer gathers evidence, such as gameplay footage and logs.
- Analysts manually review the evidence to determine if the player is cheating.
- If the player is found to be cheating, the developer takes action, such as banning the player from the game.
This process can be slow and inefficient, with several drawbacks:
- Slow detection (days or weeks).
- Limited scope.
- Human bias leads to false positives/negatives.
Sensor-based ML solution:
- Sensor data from a player’s device, such as touch metrics, gyroscope, and accelerometer, is collected.
- Machine learning algorithms analyze the data in real-time to identify patterns that are indicative of cheating.
- Once the AI detects suspicious activity, bias-free, the player is flagged for applying the relevant policy.
- As the false-positive rate is low, down to effectively non-existent, there is no need for manual review.
- If needed, provide flagging reasons and evidence for a quick manual review process (AI explainability).
Additional benefits include:
- Personalized Player Profiles: AI analyzes individual, non-aggregated sensor data patterns. This allows for more accurate identification of anomalies and suspicious behavior specific to individual playstyles.
- Scalability for Diverse Environments: Sensor-based AI solutions adapt to different game environments and player bases, making them effective for online games of varying sizes and genres.
What news does it bring for game publishers?
Sensor-based AI translates to catching cheaters within minutes (not days), achieving maximum recall, liberating developers from manual work, resulting in no false bans (happy players), and providing non-skewed clearer data insights for superior games. Developers no longer need to continually introduce new event logs to track down cheaters, adjust their anti-cheat solutions with the release of new versions, or grapple with a high false positive error rate.
As cheating in online games evolves, anti-cheat mitigation strategies are not staying behind. Emerging methods go beyond traditional detection by scrutinizing subtle player behaviors and harnessing ML engines, thus uncovering concealed patterns and adapting to fresh tactics. This not only ensures a fairer gaming environment but also liberates resources for game developers to focus on improvements and innovation, ultimately enhancing the player experience.