Cheat Prediction and Contrastive Analysis in Games Based on

Machine Learning Methods

Xingjie Lin

School of Computer and Software, Dalian Neusoft University of Information, Dalian,116023, China

Keywords: Machine Learning, Game Cheating Detection, Decision Tree, Logistic Regression, Random Forest

Abstract: The research topic of this study is cheat prediction in games and its importance lies in maintaining a fair and

enjoyable gaming environment. The main idea of this article is exploring machine learning models of game

cheating detection. Specifically, the focus of this study is on the criteria and search strategies for analyzing

the four models. Machine learning has significant advantages in generalization of data fitting. The implicit

relationships between data are further inferred and represented. The comparative analysis of those models

revealed that Logistic Regression model outperformed others with 90% accuracy. The results show that

Logistic Regression has competitive performance due to its unique features. This study provides valuable

insights for game developers and researchers to develop effective cheat detection systems. The future

prospects and directions for cheat detection research are also discussed. Overall, this research contributes to

the advancement of cheat detection in games, which can promote fairness and trust in the gaming

community.

1 INTRODUCTION

In recent years, due to the rapid developing of

machine learning, significant progress has been

made in research in the area of artificial intelligence.

Machine learning enables computers to study and

create predictions and decisions lines from different

data. This technology is essential for analyzing large

datasets and identifying patterns, making it

increasingly important for solving complex

problems in domains such as healthcare, finance,

and transportation. Many researchers have explored

different machine learning algorithms, they use

different models to solve various problems in fields

(Pinto et al. 2021).

The target of the study is analyzing the

performances of different machine learning

algorithms in monitoring game cheating. Relevant

features will be extracted from game data to identify

patterns indicative of cheating behavior. Various

machine learning models will be experimented with

to build predictive models that can accurately

classify cheating instances. The performance of

different models will be compared and analyzed to

determine their strengths, weaknesses, and

suitability for real-world game cheating detection

scenarios (Davar et al. 2022). Game cheat detection

is an important issue that can significantly impact

the fairness and integrity of the gaming experience.

This study explores the implications and practical

applications of using machine learning to enhance

the overall gaming experience for players. This

study introduces four machine learning models

(Zeng 1996, Zhang et al. 2021, Wang et al. 2016).

Anomaly detection and pattern recognition are also

discussed as useful techniques for detecting cheating

behavior. Implementing cheat detection through

neural networks is also explored in this study,

covering topics such as data collection and

preprocessing, construction of neural network

models, feature engineering, sample labeling, model

training and optimization, as well as model

evaluation and deployment (Zhang 2016, Wang

2014). Additionally, the issue of imbalance in game

cheat detection and artificial intelligence is

addressed, discussing various methods such as

imbalanced data collection, sample resampling, class

weight adjustment, anomaly detection techniques,

and ensemble learning (Lu 2007, Fang et al. 2011,

Che & Geng 2013).

The following is the layout of this article in part

2, a comprehensive analysis and discussion were

conducted on the different roles and ways in which

464

Lin, X.

Cheat Prediction and Contrastive Analysis in Games Based on Machine Learning Methods.

DOI: 10.5220/0012821100004547

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Data Science and Engineering (ICDSE 2024), pages 464-469

ISBN: 978-989-758-690-3

four different models, play in cheating detection in

games. The strengths and limitations of each model

are examined, highlighting their effectiveness in

identifying cheating behaviors. In part 3, the results

of the analysis are presented and discussed. The

findings explain the capabilities of different models

and their respective contributions to cheat detection.

Comparisons are made between the models,

examining their performance, accuracy, and

efficiency in detecting cheating behaviors. Part 4

summarizes the conclusions and future prospects of

this study. The importance of implementing robust

cheat detection systems in maintaining fairness and

integrity within the gaming community is reiterated.

Furthermore, future prospects and directions for

research in cheat detection are outlined, emphasizing

the need for continuous updates and improvements

to combat evolving cheating tactics.

METHODOLOGY

The general process for game cheat detection

involves several steps, as shown in the Figure 1.

First, relevant player behavior data and features are

collected, such as game time, score, and action

frequency. The next step is to model the probability

of a player cheating based on these features. Logistic

regression calculates the likelihood of cheating,

while decision trees partition the feature space to

classify players as cheaters or non-cheaters. To

improve accuracy and robustness, random forests

can be employed by combining predictions from

multiple decision trees. Another approach is to

utilize Support Vector Machines (SVMs), which

consider player behavior features like game

command sequences and action frequencies to

classify players into cheat or non-cheat categories.

Lastly, performance analysis is conducted to

evaluate the effectiveness of these methods in

detecting and classifying cheating behaviors in

games.

Figure 1: The process of this study (Photo/Picture credit: Original).

2.1 Logistic Regression for Game

Cheat Detection

Logistic regression is a widely used model that can

be used to detect cheating behavior in games. In this

model, the probability of a player cheating is

estimated based on their behavior and relevant

features. This is a classification algorithm that

detects whether players are cheating (Che & Geng

2013, Ciocca et al. 2015).

In the context of game cheat detection, logistic

regression analyzes various behavioral aspects of

players, such as game time, score, and action

frequency. These features are used to build a

mathematical model that calculates the likelihood of

a player engaging in cheating activities. The model

assigns a probability value between 0 and 1, where a

value close to 1 indicates a higher probability of user

cheating.

To apply logistic regression in game cheat

detection, a training dataset is collected, consisting

of labeled instances where cheating behaviors are

known. The model learns from this dataset to

identify and judge the relationship between player's

own behavior and cheating behavior. As long as it is

trained, the model can be used to predict the

cheating probability of new instances (Murata et al.

2016).

When a player engages in the game, their

behavior is observed and fed into the logistic

regression model. Based on the calculated

probability, the model can classify the player as

either a potential cheater or a non-cheater. This

information can then be used by game developers or

administrators to take appropriate actions, such as

implementing additional monitoring or enforcing

penalties.

Overall, logistic regression plays a vital role in

game cheat detection by using player behavior and

relevant features to estimate the probability of

cheating. Its ability to classify players based on

cheating likelihood assists in maintaining fair

Cheat Prediction and Contrastive Analysis in Games Based on Machine Learning Methods

465

gameplay and preserving the integrity of the gaming

environment.

2.2 Decision Tree for Game Cheat

Detection

Decision trees are widely used in cheat detection for

games. Decision tree is an algorithm that divides the

feature space based on the player's behavioral

characteristics, such as game duration, score, and

operation frequency. It creates a hierarchical

structure of decisions and outcomes, which enables

the classification of players as cheaters or non-

cheaters (Shenoy 1998).

In the application of game cheating detection,

decision trees analyze player behavior by evaluating

different characteristic behaviors. This algorithm

starts from the root node of the entire dataset and

recursively segments the data based on the selected

features. Each segmentation node is selected by

finding features and thresholds that better classify

instances into categories of cheaters and non-

cheaters.

As the decision tree grows, it creates branches

and leaves that represent different decisions and

outcomes. For instance, if the game time exceeds a

certain threshold and the score is unusually high, the

decision tree may classify the player as a potential

cheater. On the other hand, if the frequency of

actions is within expected limits, the player may be

classified as a non-cheater (Hailemariam et al.

2011).

To apply decision trees in cheat detection, a

training dataset is required, consisting of labeled

instances where cheating behaviors are known. The

decision tree algorithm learns from this dataset to

construct an optimal tree structure that maximizes

the accuracy of classification. Once trained, the

decision tree can classify new instances and use it to

determine whether players may cheat.

The path from the root to the leaf in the decision

tree shows all if else conditions that lead to

classification results, providing interpretable results.

This allows game developers or administrators to

gain insights into the specific behaviors that indicate

cheating and take appropriate actions accordingly.

In summary, decision trees are a valuable tool for

cheat detection in games. By partitioning the feature

space based on player behavior features, decision

trees can classify players as cheaters or non-

cheaters, providing actionable information for

maintaining fair gameplay and ensuring the integrity

of the gaming environment.

2.3 Random Forest for Game Cheat

Detection

Random forests are a powerful technique used in

cheat detection for games. Random forest integrates

multiple decision trees to ensure the accuracy of its

data and robustness of cheating detection models.

By aggregating predictions from individual trees,

better performance can be achieved in identifying

cheating behavior compared to decision trees (Zhao

et al. 2018).

In the application of game cheating detection,

random forest consists of serious of decision trees,

everyone of that is trained on different subsets of

relevant data. The trees in the random forest are

constructed by randomly selecting features and data

samples. This randomness helps to decorrelate the

trees and reduce overfitting.

When a new instance needs to be classified, each

tree in the random forest independently predicts

whether the player is a cheater or a non-cheater. The

final classification is determined by synthesizing

predictions from various trees. The majority of

voting items in the decision tree determine the final

classification of the user.

Random forests offer several advantages in cheat

detection. Firstly, they improve the accuracy of

predictions by reducing the variance associated with

individual decision trees. The ensemble of trees

helps to capture a more comprehensive range of

cheating behaviors and generalizes well to unseen

instances. Secondly, random forests are robust to

irrelevant features such as noise. They can process a

large number of features without overfitting, making

them more suitable for complex cheating detection

scenarios (Liu et al. 2012).

In practical applications, game developers or

administrators can utilize random forests to detect

cheating behaviors in real-time. By continuously

monitoring player behavior and feeding it into the

random forest model, cheating instances can be

identified promptly. Appropriate actions, such as

issuing warnings or applying penalties, can then be

taken to maintain fair gameplay and protect the

gaming environment.

In conclusion, random forests are a valuable tool

in cheat detection for games. By integrating

predictions from multiple decision trees, Random

Forest enhances the accuracy of cheating detection,

effectively identifies cheating behavior, and ensures

a gaming experience for players.

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466

2.4 SVM for Game Cheat Detection

Support vector machines can be used to model and

detect cheating behavior in games. SVMs are one of

the supervised learning methods used in

classification, regression, or other tasks. In the

context of cheat detection, SVMs help classify

players into cheat or non-cheat categories by

considering player behavior features such as game

command sequences and action frequencies (Ferretti

2008).

The basic idea behind SVMs is to find the

optimal decision boundary which distinguishes the

two sets of data with the maximum margin. The

training of SVM maps input data points to a high-

dimensional space, where hyperplanes can be

constructed to separate these two classes. A

hyperplane is a decision boundary that maximize

margin. The point closest to the hyperplane is the

support vector, commonly used to determine the

position of the hyperplane. The goal of SVM is to

minimize classification errors while maximizing

margin.

The SVM model learns from this dataset to

construct an optimal decision boundary that

classifies new instances into cheat or non-cheat

categories based on their behavior features. For

example, if a player's game command sequence is

significantly different from that of other players, the

SVM model may classify the player as a potential

cheater.

SVMs have several advantages in cheat

detection. Firstly, they can handle high-dimensional

data and work well with small datasets. Secondly,

they are effective in identifying complex cheating

behaviors that involve multiple variables. SVMs are

also robust to noisy or irrelevant features, making

them suitable for real-world applications (Ferretti

2009).

In practical applications, game developers or

administrators can use SVMs to monitor player

behavior in real-time and identify potential cheaters.

By continuously feeding player behavior features

into the SVM model, cheating instances can be

detected promptly, and appropriate actions can be

taken to maintain fair gameplay and protect the

gaming environment.

In conclusion, SVMs are a valuable tool in cheat

detection for games. By considering player behavior

features and constructing an optimal decision

boundary, SVMs can classify players into cheat or

non-cheat categories, enabling effective

identification of cheating behaviors and maintaining

the integrity of the gaming experience.

RESULT AND DISCUSSION

This chapter provides machine learning techniques

for deep analysis of cheating detection in games. It

aims to equip game developers with practical

insights and guidelines to improve the integrity and

fairness of their games while enhancing the gaming

experience for players.

This chapter first investigates models that is well

used for game cheating detection. Those models

were discussed in detail. Each technique is explained

in terms of its strengths, weaknesses, and suitability

for cheat detection in games.

In addition to machine learning techniques, the

chapter explores various types of variables that can

be considered in cheat detection. These variables

encompass both discrete and continuous aspects

related to game actions, data, events, player input

behaviors, and network data. Understanding these

variables provides valuable insights into player

behavior patterns, enabling the identification of

anomalies indicative of cheating.

Figure 2: Using HaarPSI measurement ϵ the impact on perceived image quality (Aditya et al. 2013)

Cheat Prediction and Contrastive Analysis in Games Based on Machine Learning Methods

467

Figure 3: Using different cost functions to train the numerical values of the same DNN architecture (Aditya et al. 2013)

The above two figures represent a series of

research and data analysis on the topic of "Machine

Learning-Based Game Cheating Analysis" published

by Aditya Jonnalagadda et al. on March 18, 2021

(Aditya et al. 2013).

The researchers discuss the measurement of true

positives in detecting cheating frames and how it is

affected by adversarial attacks. It compares the

performance of a deep neural network (DNN)

detector trained with different loss functions: one

without any protection against attacks and another

with protection using uncertainty loss and interval

bound propagation (IBP). The ratio of true positives

preserved under attack is used as a representative of

the network's defense ability (compare with Figure

2).

Researchers investigated the effects of loss

function and IBP on the performance of DNN under

adversarial attacks through experiments. This

experiment was conducted in ε Under consistent

conditions, aiming to balance attack intensity and

frame quality. The results indicate that without IBP,

the entire dataset may be vulnerable to attacks

(compare with Figure 3).

Furthermore, the chapter offers a comprehensive

discussion on the implementation of cheat detection

using neural networks. It covers crucial steps such as

data collection and preprocessing, building the

neural network model, feature engineering, labeling

samples, model training and optimization, and

model evaluation and deployment. This detailed

walkthrough ensures game developers have a clear

understanding of the entire process.

Addressing the issue of class imbalance in cheat

detection is another key focus of the chapter. The

imbalance between cheating and non-cheating

instances can pose challenges in accurately detecting

cheating behaviors. To overcome this, the chapter

presents various solutions, including imbalanced

data collection, sample resampling, class weight

adjustment, anomaly detection approaches, and

ensemble learning. These strategies assist in

improving the overall performance of cheat

detection models.

In conclusion, this chapter provides a

comprehensive overview of cheat detection in

games, offering practical insights into machine

learning techniques and data analysis methods. By

implementing these guidelines, game developers can

effectively identify and deter cheating behaviors,

ensuring fairness and integrity within their games.

However, it is important to acknowledge that cheat

detection is an ongoing battle, as cheaters

continually develop new tactics. Therefore,

continuous research, monitoring, and updates to

cheat detection algorithms are crucial to stay ahead

of emerging cheating techniques.

By leveraging the knowledge presented in this

chapter, game developers can establish robust cheat

detection systems that promote a more enjoyable and

equitable gaming environment for all players.

Through constant vigilance and adaptation,

developers can maintain the integrity of their games,

fostering a positive gaming community.

CONCLUSION

In summary, the use of machine learning technology

for cheating detection in game management is

crucial to ensuring fairness and honesty in the game.

These four models are frequently used approaches

that can effectively identify cheating behaviors. By

analyzing various relevant variables in the game,

developers can obtain valuable information on the

player's behavior patterns and operating habits,

thereby accurately detecting cheating situations.

To implement cheat detection using neural

networks, developers must follow several crucial

ICDSE 2024 - International Conference on Data Science and Engineering

468

steps, including data collection and preprocessing,

model building, feature engineering, sample

labeling, model training and optimization, and

evaluation and deployment. Addressing the issue of

class imbalance is also important, as it poses

challenges in accurately detecting cheating

behaviors. Some solutions such as imbalanced data

collection, sample resampling, class weight

adjustment, anomaly detection methods, etc. can

also help improve the overall performance of game

cheating detection models. However, it is important

to acknowledge that cheat detection is a continuous

battle, as cheaters continually develop new tactics.

Therefore, continuous research, monitoring, and

updates to cheat detection algorithms are crucial to

stay ahead of emerging cheating techniques. In

conclusion, game developers can leverage the

insights presented in this study to enhance the

gaming experience for all players. By implementing

effective cheat detection systems, developers can

maintain the fairness and integrity of their games,

fostering a positive gaming community. The next

step for developers is to continue refining and

improving cheat detection algorithms to stay ahead

of evolving cheating tactics and ensure a fair gaming

environment for all players.

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