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.