The cost of cheating in online gaming: How it affects the industry

albo 26.11.2023
The cost of cheating in online gaming: How it affects the industry

Multiple recent research have found that 60% of online gamers feel that their multiplayer gaming experience has been negatively impacted due to other cheating players.

In the world of online gaming, cheating is like that annoying kid at the playground who keeps bending the rules to win. It’s not cool, it’s not fair, and it ruins the fun for everyone else. But in this article, we discuss how cheating leads to far-reaching consequences in the gaming industry as a whole.

The costs of cheating span from industry efforts to detect and limit it, to losses in monetization to brand reputation issues and to victims’ monetary losses due to scams. 

 Cheating is prevalent in most multiplayer online games, though measuring its full extent remains challenging. Various methods of cheating in online games can take the form of software (scripts, automation, moding) assistance or various forms of unsporting play taking advantage of exploits within the game. The Internet and darknets offer many tools and methodologies built to cheat in online games, such as this content piece.

A closer look into the online gaming cheating phenomena


The evolution of cheating in online gaming:


From classic aimbots and wall hacks to sophisticated cheat detection systems, the cat-and-mouse game between developers and cheaters has reached new heights. 

In the early days of online gaming, cheating was primarily limited to exploiting glitches and using rudimentary aimbots. These methods were relatively easy to detect and counter, and game developers quickly implemented measures to address these vulnerabilities.

As multiplayer and esports games gain prominence, the methods of cheating have become more intricate, enforcing constant innovation in anti-cheat measures. 

Cheaters began to utilize software that could analyze game data and make automated decisions, giving them superhuman reflexes and unbeatable aim. These advancements made cheating more difficult to detect and counter, as the lines between legitimate player behavior and automated cheating became increasingly blurred.

Maybe the most important aspect of this phenomenon is the fact that cheaters are constantly adapting their tactics to overcome anti-cheat technologies. 

 

Let’s draw a timeline of the evolution of cheating: 

  1. Early Days:
  • Methods: Glitch exploitation, basic aimbots.
  • Detection and Countermeasures: Developers quickly respond to glitch exploitation, implementing measures to fix bugs and vulnerabilities.
  1. Growth of Multiplayer games (~2010):
  • Methods: Introduction of more advanced aimbots, and wallhacks.
  • Detection and Countermeasures: Developers enhance anti-cheat measures as multiplayer gaming gains popularity. Early stages of signature-based detection.
  1. Esports Emergence (~2010+):
  • Methods: Automated decision-making software for superhuman reflexes.
  • Detection and Countermeasures: Anti-cheat measures evolve with a focus on heuristic analysis. Increased sophistication in cheat tactics requires constant innovation in detection systems.
  1. Technological Advancements (2015 and onwards):
  • Methods: Machine learning-powered automation, cloud-based cheating services.
  • Detection and Countermeasures: Developers integrate machine learning algorithms into anti-cheat systems. Cloud-based cheating services pose new challenges.
  1. Present Day (2023):
  • Methods: Device sensor-based analytics, adaptive cheats using machine learning.
  • Detection and Countermeasures: Introduction of real-time sensor-based analytics for immediate identification of suspicious behavior. Ongoing arms race with machine learning-powered cheats.
  1. Future Challenges (Beyond 2023):
  • Anticipated Methods: Continued use of machine learning-powered cheats, challenges in cloud gaming and cross-platform play.
  • Evolution in Detection: Enhanced sensor-based ML analytics to counter emerging threats. Focus on minimizing false positives through robust evidence collection.

 

 

Common types of cheating: 

Software-based cheats:

  • Aimbots: These programs automatically aim at opponents, giving players an unfair advantage in shooting games.

  • Wallhacks: These enable players to see opponents through walls.

  • Speedhacks: These programs increase the speed of a player’s character, making them difficult to catch.

  • Lag switches: These programs artificially induce lag, causing the player’s character to appear to teleport around the map, making them difficult to hit.

Exploitation of game mechanics:

  • Glitching: This involves taking advantage of unintended bugs or glitches in the game to gain an unfair advantage.

  • Boosting: This involves collaborating with other players to artificially inflate one player’s rank or statistics.

  • Farming: This involves using automated scripts or bots to perform repetitive tasks in the game, such as gathering resources or killing enemies.

Hardware-based cheats:

  • Game console mods: These mods allow players to install custom software on their game consoles, which can be used to cheat.

  • Input modifiers: These devices allow players to input commands into the game that are not normally possible, such as rapid fire or macros.

  • Memory editors: These devices allow players to modify the game’s memory in real time, which can be used to cheat.

Other, more sophisticated methods include: 

  • Data Manipulation: Cheaters can manipulate game data packets to gain an unfair advantage. This involves intercepting and modifying data exchanged between the game server and the player’s device. By altering data related to player positions, resources, or inventory, cheaters can gain control over the game world and manipulate outcomes.
  • Reverse Engineering and Code Injection: Cheaters with programming expertise may resort to reverse engineering game code to identify vulnerabilities and inject malicious code. This injected code can alter game mechanics, bypass anti-cheat measures, or grant cheaters unauthorized privileges.
  • Machine Learning-Powered automation: Advanced cheaters employ machine learning algorithms to create sophisticated software that can learn and adapt to game environments. These cheats can mimic human behavior, making them more difficult to detect by traditional anti-cheat software.
  • Cloud-Based Cheating Services: Cheaters can utilize cloud-based services to access remote cheating tools and resources. These services may provide access to aimbots, wallhacks, or other cheating software, making it easier for cheaters to evade detection.
  • DDoS (Distributed Denial-of-Service) Attacks: Cheaters may launch DDoS attacks to disrupt game servers and gain an unfair advantage. These attacks overwhelm servers with traffic, causing them to crash or become unresponsive, giving cheaters a temporary advantage in the game.
  • Social Engineering and Collusion: Cheaters may employ social engineering tactics to trick other players into revealing sensitive information or gain access to their accounts. This information can then be used to exploit vulnerabilities or manipulate game mechanics.
  • Hardware Tampering: Cheaters may tamper with hardware components, such as game controllers or input devices, to gain an unfair advantage. This may involve modifying hardware to achieve rapid-fire capabilities or altering input signals to manipulate game mechanics.

 

To summarize this part, even the top industry gaming professionals, such as game managers, product managers, and alike are not aware of all these methods described above or the extent to which they are used. 

 

Real time examples for major cheating incidents covered by the media, include: 

Call of Duty

source

The publisher has taken action against a massive wave of cheating, banning over 50,000 accounts from the game. This helped to restore some semblance of fairness to the game, but nonetheless, it is estimated that the cheating plague caused major losses to the brand.  

Lately it had to ban 14K accounts again> 

Moreover, in its effort to satisfy honest players and support fair play, Call of Duty now shows you when a cheater has been booted right in the kill feed. source  

Destiny 2

Another example includes this recent news of ‘Destiny 2’ developer Bungie winning $6.7M in lawsuit against video game cheat-maker. source

The article states that:  “It’s an endemic problem in online gaming, particularly on the PC platform” 

Overwatch 2

An additional example is Overwatch 2 which has banned 250,000+ cheaters since launch.

Valve 

Valve banned 40,000 Dota 2 cheaters; The company said it was able to spot players that read ‘secret’ data not available during normal gameplay, as part of an ongoing campaign to reduce cheating in the game (Source) 

There are many more examples, and one can say that the industry as been busy fighting this pandemic for quite some time now, with only partial success. 

 

The cost of cheating: A multifaceted burden on the gaming industry

Cheating in online gaming creates more than just frustration—it messes with fair play, saps player excitement, increases costs, and hurts brand names. Recognizing these issues is key for game makers and industry players to tackle this widespread problem head-on. 

We can split this discussion into discrete areas: 

 

The impact of cheating on IAP and player engagement


Cheating directly impacts in-app purchases (IAP) by deterring legitimate players from spending money on a game if they perceive that the playing field is uneven. A study revealed that 60% of gamers across the globe have had their multiplayer gaming experience negatively impacted by other players cheating. And 77% are likely to walk away from a multiplayer online game if they feel that other players are gaining an unfair advantage through cheating. 

This translates into lost revenue for game developers, who rely on IAP to sustain their businesses – another study shares that 48% of online gamers said that they would buy less in-game content as a result of cheating

Moreover, cheating erodes player engagement and retention, as players become discouraged by unfair competition and seek out more enjoyable gaming experiences. A survey by Newzoo revealed that 57% of gamers have quit a game due to cheating, highlighting the detrimental impact on player loyalty.

 

The cost of cloud 


The increased traffic generated by cheating tools can overload game servers, leading to performance issues and additional costs associated with server maintenance and upgrades.

 

Industry estimates of the financial costs of cheating


Industry experts estimate that cheating costs the gaming industry billions of dollars annually in lost revenue, increased operational expenses, and reputation issues. For example, this resource reports that the industry lost $29 billion in 2019 alone, due to video game cheating. 

 

The indirect impact of cheating on different areas


Beyond the direct financial costs, cheating also has indirect consequences that affect various aspects of the gaming experience.

Competitive Gameplay: Cheating damages the gamer experience which leads to a decline in viewership and sponsorship opportunities.

Overall Gaming Experience: Cheating creates frustration among legitimate players. This leads to a decline in player engagement and increases churn. 

Game Reputation: Cheating can lead to negative publicity around the game brand, damaging brand image and future revenue potential. 

 

Combating cheating: The industry’s arsenal


In the battle against cheating, the gaming industry has deployed a multifaceted arsenal of countermeasures, ranging from sophisticated anti-cheat software to comprehensive cheat mitigation policies.

 

Anti-cheat software and technologies: The first line of defense


At the forefront of the anti-cheating fight lies a diverse array of software and technologies designed to identify and neutralize cheating attempts. These tools employ various techniques, including:

  • Signature-based detection: This method involves identifying and blocking known cheating patterns or signatures within game code.
  • Heuristic analysis: This approach analyzes player behavior and game data to detect anomalies indicative of cheating, such as superhuman reflexes or impossible movements.
  • Machine learning-based tools: Advanced algorithms are trained on vast amounts of data to identify patterns and anomalies that could signal cheating activity.
  • Kernel-level protection: Deeply embedded anti-cheat software can monitor and intercept system calls, gaining access to a lower level of the game’s operation to detect and prevent cheating attempts.


Cheat mitigation policies: Beyond detection; addressing the root causes


While anti-cheat software and technologies play a crucial role in detecting and preventing cheating, cheat mitigation policies address the underlying causes of cheating. These policies outline clear guidelines on acceptable behavior, penalties for violations, and appeal processes.

It is important to mention here that identifying cheaters doesn’t necessarily mean that companies will prevent them from playing – it depends on the impact on the game community and the game’s monetization. In the past, companies chose to either turn a blind-eye to cheating as long as their user base was growing or simply ban players for a lifetime to keep the game clean. Today, gaming companies evolved their policies but still, they need advanced anti-cheat detection tools in order to have full visibility over what’s going on in their games. For instance, if players use simple cheats to improve their own experience without impacting others, companies may decide not to ban them.  Understanding cheaters’ motivation also contributes to optimizing the game, so players will get what they want without using cheats. 

When cheaters lead to monetization loss, game publishers insis on a rapid action that stops this impact. 

By combining sophisticated anti-cheat technologies with comprehensive and dynamic cheat mitigation policies, the gaming industry can create a fairer and more enjoyable experience for all players. 

 

Problems and limitations: 

However, things aren’t perfect…

Most anti-cheat technologies often fall short of addressing the ever-evolving tactics of cheaters, leaving loopholes that can compromise fair gameplay. Moreover, some tools have limited scope and can’t identify new or modified cheating methods. Some tools require high maintenance. False positives are also a major concern, as they result in intolerable inaccuracies that unjustly subject innocent players to policing. Several of these tools introduce privacy-related challenges and there are other limitations not mentioned here.  

 

The role of machine learning: adaptive defense against evolving threats


Machine learning (ML) has emerged as a powerful tool in the anti-cheating arsenal. ML algorithms can continuously analyze vast amounts of game data, identifying patterns and anomalies that indicate cheating activity. This adaptive approach allows anti-cheat systems to stay ahead of evolving cheating methods, as ML can learn and adapt to new threats.  

But, there’s a problem, again… 

The role of machine learning in the battle against cheating is a double-edged sword. While it empowers anti-cheat systems to detect sophisticated techniques, it also provides cheaters with tools, like ChatGPT or Google Bard, to create advanced cheating software that can deal with the newest anti-cheat tools.
This leads to an ongoing arms race, requiring constant refinement of anti-cheat algorithms. 

So, what’s the deal? 

 

The missing element- Device Sensor-Based In-Game Analytics


As mentioned, traditional anti-cheat methods, while effective to some extent, often fall short in addressing the sophistication and adaptability of modern cheating techniques. 

Gaming devices are equipped with a plethora of sensors that capture a wealth of data about user actions and interactions with the device in real-time. These sensor signals, ranging from accelerometer and gyroscope data to touch and pressure inputs, provide the most valuable insights into player behavior and identify anomalies in a way that no other technology can. 

For instance, a sudden spike in movement speed or an unnatural precision in aiming can be indicative of the use of aimbots or wallhacks. Similarly, discrepancies between in-game actions and sensor data can reveal attempts to manipulate the game mechanics.

Sensors help us understand human behavior in its most refined/raw form, and are very hard to trick. 

 

The Benefits of Using Mobile Device Sensor Data for Anti-Cheat Purposes


Incorporating device sensor data into anti-cheat strategies offers several key advantages:

  • Real-time detection enabling immediate identification of suspicious behavior and cheating attempts.
  • Granular data analysis that allows a much more precise and nuanced analysis of potential cheating activities compared to all other tools. 
  • Dealing with evolving threats by identifying patterns and anomalies in real time.
  • Privacy is fully kept as the technology doesn’t include a private-data sensor such as camera, microphone and does not collect PII (personal identifying information).
  • Clear visual proof of the cheating behavior can be generated.


Addressing the everlasting ML-based arms race with sensor-based analytics:  

By focusing on real-time data from in-game sensors rather than solely relying on algorithmic detection, this approach introduces a more immediate and context-aware defense against cheating tactics. This shift minimizes the reactive nature of traditional anti-cheat methods, providing game developers with a dynamic and effective means to stay ahead in the ongoing battle against gaming misconduct.

 

Overcoming traditional game analytics gaps

Sensor-based analytics can identify subtle and dynamic patterns in player behavior and device interactions that event-based analytics cannot detect, enabling the recognition of sophisticated cheating methods that would otherwise go unnoticed.

For instance, event-based analytics may be able to identify a player who suddenly teleports across the map, indicating the use of a wallhack. However, it would struggle to detect a more sophisticated wallhack that dynamically adjusts its behavior to avoid detection. Sensor-based analytics, on the other hand, can continuously monitor the player’s movement and device interactions, identifying subtle changes in aiming patterns, movement speeds, and input timing that may indicate the use of such a cheat.

 

How does it work? A couple of examples: 

Speedhack Detection

Speedhack cheating involves using software that increases the speed of a player’s character, making them difficult to catch. Sensor-based AI solutions can detect speedhacks by analyzing data from the device’s accelerometer and gyroscope. These sensors track the movement of the device, which can reveal unnatural movement speeds that are indicative of speedhack usage. For instance, a speedhacker might exhibit an unrealistic acceleration or maintain a constant speed that exceeds the limitations of the game’s physics. Sensor-based AI algorithms can identify these patterns and flag the player for further investigation.

 

Recoil Control Cheating

Recoil control cheating involves using software that automatically compensates for the recoil of a weapon, making it easier to maintain accurate fire. Sensor based analysis can reveal it. For instance, a recoil control cheater might exhibit a consistent downward movement of the device, compensating for the upward recoil of the weapon. 

 

Rapid-Fire Cheating

Rapid-fire cheating involves using an external controller that can send multiple inputs to the game in a very short period, resulting in an abnormally high rate of fire. Sensor-based AI solutions can detect rapid-fire cheating by analyzing data from the external controller’s input events. These events include button presses, joystick movements, and other actions that are registered by the controller.

 

Unrealistic Aiming Assistance

Unrealistic aiming assistance cheating involves using an external controller that provides aimbot-like assistance to the player. For instance, a cheater using an unrealistic aiming assistance device might exhibit an unnatural smoothness in their aim or a constant lock-on to opponents. Sensor-based AI algorithms can analyze the timing, precision, and consistency of input events along with the movement patterns of the device to identify these anomalies and flag the player for further investigation.

 

Adaptive Wallhack and Aimbot

Traditional anti-cheat tools often rely on signature-based detection and heuristic analysis, which can be ineffective against sophisticated cheating methods that adapt to countermeasures. Sensor-based AI solutions, on the other hand, can continuously learn and adapt to identify new cheating techniques, including adaptive wallhack and aimbot.

Adaptive wallhack and aimbot utilize machine learning algorithms to analyze in-game data and sensor inputs, constantly adjusting their behavior to circumvent traditional detection methods. These cheats may employ techniques like dynamic aiming patterns and real-time adjustments based on player actions, making them difficult to identify using conventional approaches.

Sensor-based AI solutions can overcome these limitations by continuously monitoring player behavior, device interactions, and sensor data. AI algorithms can analyze patterns and anomalies in real time, identifying subtle changes in aiming patterns, movement speeds, and input timing that may indicate adaptive cheating.

Event-based analytics, which relies on analyzing discrete events in the game, falls short in detecting adaptive cheating methods due to its inability to capture the continuous and dynamic nature of these cheats..

 

Pretends to be human, fulls “regular” in-game analytics: 

For instance, an adaptive aimbot might exhibit slight variations in its aiming trajectory, appearing less predictable and more human-like to traditional anti-cheat tools based on event-based analysis. However, sensor-based AI algorithms can easily detect these subtle variations.

 


The Future of Online Gaming Cheating


The world of online gaming is constantly evolving, with new game mechanics, complex environments, and emerging technologies creating new opportunities for cheaters to exploit. Traditional cheating methods, such as aimbots and wallhacks, continue to pose a threat, but new techniques are emerging that are more difficult to detect and counter.

 

New and Emerging Cheating Methods


Machine learning-powered cheats are becoming increasingly prevalent, utilizing algorithms to analyze in-game data and adapt their behavior to avoid detection. These cheats can dynamically adjust aiming patterns, movement speeds, and input timing, making them difficult to distinguish from legitimate player actions. 

Moreover, the rise of cloud gaming and cross-platform play introduces new challenges for anti-cheat measures.

 

How Technology Will Evolve as Well


As a consequence, anti-cheat technologies are (and will continue to) adapt as well.  Sensor-based ML analytics, which leverages data from device sensors like accelerometers and gyroscopes, offer an example of such adaptation.

 

False Positives: A Critical Concern


One of the biggest challenges with cheating is the need for accuracy. False positives, where legitimate players are mistakenly identified as cheaters, can have severe consequences, causing frustration, alienating players, and damaging the game’s reputation. Emerging anti-cheat solutions will employ robust evidence-collection and analysis techniques. 

Sensor-based data and AI insights should be combined with traditional anti-cheat methods to create a comprehensive and reliable detection system.

 

Evidence-Based Decisions: The Key to Effective Anti-Cheat


Evidence plays a crucial role in ensuring the accuracy and fairness of anti-cheat measures. By collecting and analyzing a wide range of data, game developers can gain a deeper understanding of cheating patterns and make informed decisions about how to combat them.

This evidence-based approach allows for more targeted and effective anti-cheat measures, reducing the risk of false positives and ensuring that legitimate players are not unfairly penalized.

In conclusion, the future of online gaming cheating presents both challenges and opportunities. As cheaters become more sophisticated, anti-cheat technology must adapt and evolve, leveraging advancements in sensor-based analytics, AI, and evidence-based decision-making to maintain a fair gaming experience for all players.

 

Conclusion 


Cheating in online gaming is like that annoying kid at the playground, but in the gaming world, it’s a financial buzzkill. From aimbots to cloud-based mischief, and in terms of the cost of cheating, the industry’s bleeding billions. 

Enter anti-cheat technology, AI, and sensors—the ultimate defense against cheat wizards. With this tech arsenal, fair play is more likely to prevail in the gaming universe. 🚀🎮

** Meta description: 

Dive into the costly realm of online gaming cheats, uncovering the financial impacts and the cutting-edge anti-cheat technologies spearheading the battle for fair play. 

 

This content is brought by Quago, a provider of a unique AI-based technology that turns in-game device sensor-data into powerful insights and predictions around cheating, churn, UA fraud, player skill level and more. 
Learn more here

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