Maintaining bot-free play: Rethinking best practices for modern games

The growing usage of bots is a major problem for developers, who view botting as a threat to the integrity and fairness of their games. In response, developers have put in place a variety of anti-bot measures within their games. However, as bots become more sophisticated, it remains questionable if traditional detection methods are still effective or developers should embrace smarter solutions that can analyze real player interaction patterns.

Let’s take a closer look at the most common anti-bot methods and see how they stack up.

Client-side prevention

Real-time prevention focuses on stopping bots before they interact with the game, usually by monitoring the local system. These include random event prompts, anti-cheat, code obfuscation and keystroke inputs.

1. Random event prompts

Early anti-bot methods like random event prompts and CAPTCHA tests aimed to verify human players through random tasks or events. Some mobile titles used pop-ups or puzzles to deter bots. These techniques, however, often disrupt gameplay and frustrate legitimate users, as well as being bypassed by third-party CAPTCHA solving services integrating into bot programs. It makes these methods largely ineffective today.

2. Anti-cheat

Anti-cheat technology scans for known signatures of cheating software by monitoring the system for third-party tools or programs attempting to modify or read internal game data. The memory space of the game client is scanned, checking for patterns, code injections or unauthorized modifications. However, since they don’t interact directly with the process, traditional anti-cheat solutions may have limited effectiveness against botting.

3. Code obfuscation

This technique protects the game by obscuring its code or logic, making it harder for cheat and bot developers to reverse-engineer or modify the game. In the case of bot developers (who rely on memory reading and not pixel-based detection), this method works well at times.

4. Keystroke analysis

Memory scanning and signature-based anti-cheat methods are no longer effective without memory manipulation, known cheating software signatures or injection into the game. In such cases, bot detection is more successful when keystroke analysis is used in conjunction with behavioral analysis, as together they examine how a player interacts with the game via their keyboard and mouse. While bots try to mimic human input, analyzing the timing, frequency and variation of keystrokes, automated scripts can be distinguished from human behavior.

Server-side detection

This relies on post-action detection, which uses server-side data logs and behavior analysis to detect suspicious activities. The biggest challenge faced by gaming companies is the lack of information that can effectively identify and differentiate between humans and bots. That said, however, here are several common approaches to this kind of detection method.

1. Playing time detection

Given that bots are usually designed to run continually (carrying out the monotonous jobs), determining whether actions are being performed by real players or not can be done through unusual playtime detection systems. These measure the length of each player's uninterrupted play session.

2. Behavior detections

This examines player behavior to find characteristics that would be seen as bot behavior. A lack of variation in movement or activity, unrealistic trade frequencies, perfect timing that is impossible for a human to execute, odd activity in trade histories, chat logs, transaction logs, loot patterns or movements that repeat at precise intervals are a few examples.

3. Rate limiting

Limiting the number of commands or actions a player may submit to the server in a certain amount of time helps prevent abuse and maintain server stability. This works effectively against bots that attempt to perform many activities in a second exceeding humanly possible rate.

4. Server-side behavioral analysis

Server-side behavioral analysis is among the most advanced bot detection methods these days, making it far harder for cheaters to bypass or tamper with the detection process. By leveraging behavioral analysis, this method builds a baseline of authentic user behavior to track and analyze player data in real time. It then monitors all the interactions with the game, compares them with what is considered normal for that specific game and detects bots that are operating outside the parameters.

To make this method work effectively, the behavioral data is collected on the client side and transmitted to the servers for further analysis. Large data processing capacity is therefore essential. It enables developers to precisely detect bots by providing scalable data ingestion and accuracy. Furthermore, it is crucial for ensuring game transparency in the fight against cheating in general and bots in particular. The developers and/or publishers will be fed with as much insight as possible into why certain players are flagged as bots, avoiding false positives, guaranteeing the highest effectiveness in bot detection and ensuring a healthy gaming environment for players.

Stop bots in their tracks, start your defense now!

As bots grow more advanced, they threaten both the player experience and your gaming business’s bottom line. Let’s take them on with the right approach!