Intrusion Detection System(IDS) Development Using Tree-Based Machine Learning Algorithms

International Journal of Computer Networks & Communications (IJCNC) Vol.15, No.4, July 2023
DOI:10.5121/ijcnc.2023.15406 93
INTRUSION DETECTION SYSTEM (IDS)
DEVELOPMENT USING TREE- BASED MACHINE
LEARNING ALGORITHMS
Witcha Chimphlee and Siriporn Chimphlee
Department of Data Science and Analytics, Suan Dusit University, Bangkok, Thailand
ABSTRACT
The paper proposes a two-phase classification method for detecting anomalies in network traffic, aiming to
tackle the challenges of imbalance and feature selection. The study uses Information Gain to select
relevant features and evaluates its performance on the CICIDS-2018 dataset with various classifiers.
Results indicate that the ensemble classifier achieved the highest accuracy, precision, and recall. The
proposed method addresses challenges in intrusion detection and highlights the effectiveness of ensemble
classifiers in improving anomaly detection accuracy. Also, the quantity of pertinent characteristics chosen
by Information Gain has a considerable impact on the F1-score and detection accuracy. Specifically, the
Ensemble Learning achieved the highest accuracy of 98.36% and F1-score of 97.98% using the relevant
selected features.
KEYWORDS
Intrusion Detection System, Anomaly Detection, Imbalance Data, Feature Selection, CICIDS-2018 dataset
1. INTRODUCTION
Due to the growth of applications that produce data, the data volumes have increased
dramatically in recent years and must now be gathered, stored, and analyzed[1]. Therefore, the
number of attacks has increased including malware, botnets, spam, phishing, and DoS attacks
have turned out to be consistent dangers for systems and hosts. The network traffic activity is
made up of numerous features that have been compiled into a dataset to identify various attack
types [2]. Technology is currently facing a significant difficulty because of the daily growth in
the enormous volume of data generated online[3].In order to identify these threats, effective
intrusion detection systems (IDS) have been created. Systems for detecting intrusions have been
crucial to the safety of networks and computers. IDS network traffic monitoring and analysis is
used to categorize various sorts of attacks [4][5]. The primary problems with the IDS are the
systems' susceptibility to errors and the inconsistent and unfair ways that the systems' evaluation
processes were frequently carried out[6][7]. One of the most important challenges with the
greatest performance for big intrusion detection data sets is the component of dimensional
reduction known as feature selection, which is the process of choosing the ideal feature subset to
represent the full dataset [8].
Problems with categorization might be seen in pattern recognition or anomaly detection. When a
variable needs to be predicted yet is categorical, the challenge is referred to as a classification
problem. Several classification techniques are used to detect distinct sorts of assaults to improve
IDS performance [9]. Anomaly intrusion detection is an important research area in computer

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network security, aimed at identifying abnormal behavior in network traffic that may indicate an
attack or a security breach.
IDS that rely on detection come in two flavors: signature-based and anomaly-based. Attacks are
discovered using specified signature samples in signature-based IDS, making it a type of abuse
detection[10]. This method works well with large samples of signature data and has a low false
alarm rate. Only known attacks, however, can be identified, leading to a high proportion of
missed alarms. Nevertheless, anomaly-based IDS recognizes assaults by seeing out-of-the-
ordinary behaviors that depart from the typical profile. This method may have a reduced risk of
false alarms, but it can detect unidentified attacks.
The categorization model separates the dataset into training and testing phases[11].
Unfortunately, the training process is difficult and time-consuming due to the abundance of high-
dimensional features. To improve the model's performance during testing, pertinent and valuable
features must be chosen from the whole feature collection[ 1 2 ] . Improvements to intrusion
detection systems (IDS) are being made using machine learning (ML) techniques, which are
becoming more and more prominent in computer security datasets. There are many machine
learning algorithms available to users that can be implemented on datasets [13]. ML algorithms
assist in managing enormous amounts of data and extracting important features for different
feature selection procedures[14]. Popular machine learning classifier IDS divides different
assaults into several categories. Machine learning techniques including decision trees (DT), extra
trees (ET), random forests (RF), and XGBoost (eXtreme Gradient Boosting) are frequently used
in anomaly intrusion detection. These algorithms understand the system's typical behavior and
recognize variations from it that might indicate an attack by training on huge amounts of network
traffic data. Intrusion detection is still a crucial field of research for two reasons. The first is the
regular updating and modifying of network breaches, which results in patterns that are constantly
changing. Second, it gets simpler to explore and assess new concepts as more intrusion detection
datasets become accessible over time [15].Therefore, it is crucial to discover an optimal method
that reduces both false positives and false negatives.
The goal of this study is to analyze how the number of feature dimensions affects classification
accuracy when using attack datasets. Additionally, the study considers the impact of data sample
imbalance on classifier evaluation. To assess the quality of preprocessed data for multiple attacks
in the CSE-CICIDS-2018 dataset, various metrics were computed. Furthermore, the study
computed and discussed the performance measures of intrusion detection models that were
trained. The main contributions of this paper are:
 The feature reduction method based on the first classification outcomes and feature
importance metrics produced by Information Gain.
 A comparison of the machine learning methods DT, RF, ET, and XGBoost for IDS.
 To demonstrate the effect of preprocessing, the imbalance issue in an intrusion
detection dataset is handled using SMOTE.
The rest of the paper is organized as follows: Section 2 gives an overview of intrusion detection
and classification model. It also provides more information about imbalance data and feature
selection. Section 3 provides our methodology. Section 4 presents the results from each of the
algorithms and Section 5 concludes with the findings and discussion of the project results.

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2. RELATED WORKS
An essential component that monitors and analyzes networks to find intrusions and alert
managers to ongoing attack operations is an intrusion detection system. Intrusion detection is a
prominent study subject. First, network invasions constantly update and evolve, leading to
patterns that are constantly shifting. Second, more intrusion detection datasets are becoming
available over time, allowing for the examination and evaluation of novel strategies [16]. Low
false positives and false negatives are desirable in an intrusion detection system. The
representative training dataset's quality, however, can have a big impact on these metrics. Real-
world situations could involve a variety of difficulties, including issues with class imbalance,
mixed data types (continuous, discrete, ordinal, and categorical), as well as non-Gaussian and
multimodal distributions in intrusion detection traces that call for handling.
The field of intrusion detection systems has been examined and investigated by numerous
researchers. Colas and Brazdil [17] conducted a comparative analysis of KNN, Naive Bayes, and
SVM methods in intrusion detection systems. Their feature-based comparison showed that SVM
is more efficient and has shorter processing time, but KNN has a better classification accuracy.
One of the strengths of their study is the systematic comparison of these three popular algorithms,
which provides useful insights into their strengths and weaknesses. However, their evaluation
was limited to a specific dataset and may not generalize to other datasets.
Jiang et al. [18] developed a text categorization model that combines a one-pass clustering
approach and an improved KNN text classification algorithm. Their combination strategy showed
a significant improvement over conventional KNN, Naive Bayes, and SVM algorithms, in terms
of reducing text redundancy and enhancing text categorization. One of the strengths of their
research is the novel combination of clustering and classification methods, which can be applied
to various text mining tasks. However, their study focused only on text data and may not
generalize to other types of data.
Elejla and colleagues [19] examined a number of classification methods, including KNN, SVM,
Decision tree, Naive Bayes, and Neural network, to forecast DDoS attacks using network
monitoring. They found that SVM and Decision tree methods outperformed other algorithms in
terms of detection accuracy. One of the strengths of their study is the evaluation of multiple
classification algorithms in the context of DDoS attack detection. However, their evaluation was
limited to a specific dataset and may not generalize to other datasets.
Bahrololum et al. [20]used supervised and unsupervised Neural Network (NN) and Self
Organizing Map (SOM) methodologies in their analysis of network traffic patterns for intrusion
detection. Their study showed that NN and SOM can effectively capture the complex patterns in
network traffic data and outperform traditional statistical methods. One of the strengths of their
research is the use of advanced machine learning methods to analyze network traffic data.
However, their study focused only on a specific type of data and may not generalize to other
types of data.
Awad and Alabdallah [6] presented a weighted extreme learning machine technique to address
the problem of imbalanced classes in intrusion detection systems. Their study showed that the
weighted ELM approach can effectively handle imbalanced data and improve classification
performance. One of the strengths of their research is the development of a novel approach to
handle imbalanced data, which is a common problem in intrusion detection. However, their
evaluation was limited to a specific dataset and may not generalize to other datasets.

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Siriporn and Witcha [21] compared the performance of a number of classification methods,
including LR, KNN, CART, Naive Bayes, RF, MLP, and XGBoost. They also included a feature
selection technique employing radio frequency (RF) to enhance classification performance. Their
study showed that RF-based feature selection can improve classification accuracy for all tested
algorithms. One of the strengths of their research is the comprehensive evaluation of multiple
algorithms and feature selection techniques. However, their evaluation was limited to a specific
dataset and may not generalize to other datasets.
3. THE PROPOSED FRAMEWORK
In this paper, we suggest a machine learning (ML)-based traffic classification method for IDS
and discuss some of the drawbacks of current approaches. To address the imbalance of traffic
samples and identify important characteristics from input flows, we specifically recommend a
data pre-processing strategy that incorporates embedded feature selection and under-sampling. In
Figure 1, we present the structure for the suggested methodology, and in the sections that follow,
we elaborate on each stage.
3.1. CSE-CICIDS-2018 DATASET
This section provides an overview of the CSE-CIC-IDS-2018 dataset [22], It was suggested by
the Canadian Institute for Cybersecurity and the Communications Security Establishment (CSE)
(CIC). The dataset contains both the real-time network activity of various infiltration states and
all of the inner network traces required to calculate data packet payloads. The characteristics of
the dataset are relevant to our inquiry. The dataset contains 14 different forms of invasions,
including SQL injections, Brute Force-XSS, DoS GoldenEye assaults, DoS Hulk an attack,
Botnet, SSH brute force, DDoS-low orbit ion cannon (LOIC)-UDP attacks, DDoS-LOIC-HTTP
attacks, and Brute Force-UDP attacks[23].
Figure 1. Designing the proposed method
3.2. Data Preprocessing
An IDS must include data processing as it is the initial step in simplifying machine learning
model training. Effective data preparation has a direct impact on the classification model's
performance, and by using the appropriate procedures, technical issues with data pretreatment
can be resolved and performance levels can be increased. This section covers the precise steps

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involved in data preparation, such as data integration, cleaning, encoding, and normalization, as
well as how feature selection is used. The success of model training depends on the data
preparation processes, which have received little attention despite the vast number of samples
that have been collected.
3.2.1. Data Integration
This section covers the CSE-CIC-IDS-2018 dataset, which has 16,233,002 instances spread over
10 files with 80 features per row. The dataset comprises 14 different attack types across six
different scenarios, with attack traffic accounting for about 17% of these incidents [24]. There are
many different types of attacks in the dataset, which is enormous. We pre-processed the data and
then combined it into a single database to get the dataset ready for analysis. For simple access
and analysis, we compiled all the data from the raw-data files and placed it in a database.
3.2.2. Data cleaning
High-quality data is necessary to deliver trustworthy analytics that lead to effective and sensible
decision-making. Data cleaning is a necessary component of data pre-processing, which improves
the utility of a dataset. It ensures that the data is free of noise and errors that could cause model
technical issues. In this study, missing values and pointless attributes were removed from the
dataset using data cleaning. Timestamped samples, "Infinity," and "NaN" values were excluded.
The missing data was filled in with the mean value, and the feature values were scaled to a
standard format using StandardScaler.
3.2.3. Data encoding
Data encoding is required to transform category variables into numerical values that machine
learning algorithms may use. In our study, the labels are either "0" or "1," where "0" stands for
"Benign" and "1" for "Attack," because we are working with a binary classification problem. To
help the model comprehend the labels more accurately, we encoded them. The model would
perform poorly if the labels were not encoded since it would have trouble understanding them. As
a result, data encoding gives the model the ability to understand the labels as numerical values,
which improves how well it processes and learns from the data.
3.2.4.Normalization
For intrusion detection systems that rely on statistical features extracted from the data,
normalization is a crucial step in the preprocessing of the data. Input data must commonly be
normalized for machine learning-based techniques in order to remove bias that could result from
variations in the magnitudes of the variables' values. When there is a substantial difference
between the highest and lowest values of the data, normalization is necessary. By normalizing the
data, the range of values is normalized, which enhances the performance of the model. The most
common normalizing technique, StandardScaler, adjusts the data to have a mean of 0 and a
standard deviation of 1. The transformation of continuous and quasi-continuous features uses
standardization. By reducing the mean and scaling the data to a single variance, it normalizes the
data. This can be denoted as 𝒳scaler =
𝒳−μ
σ
, where 𝒳𝑠𝑐𝑎𝑙𝑒𝑟 generates a new value, 𝜇 is the
mean, and 𝜎 is the standard deviation, σ = √
1
𝑁
∑ (𝑥𝑖 − 𝜇)2
𝑁
𝑖=1 .

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3.3. Feature selection : Information Gain
Feature engineering is a critical step in machine learning where raw data is transformed into
useful features to enhance the predictive power of models[25]. Techniques for dimensionality
reduction and methods for feature selection are used in this procedure to help choose the most
pertinent features for training and detection. For models to perform better, proper dataset
preparation and feature selection are essential[26]. The process of feature selection entails
deleting noisy or unimportant features while selecting beneficial features from a dataset that
faithfully reflect the original data pattern. It is essential to the development of anomaly-based
intrusion detection systems because it increases computing efficiency and accuracy. Finding a
subcategory of characteristics that accurately represents the data and is necessary for prediction is
the aim of feature selection [27].It is essential to carefully select the ideal set of features in order
to increase the accuracy of the IDS model by reducing false positives and false negatives. In
addition, by simplifying the model, less characteristics in the CICIDS-2018 dataset can enhance
the model's interpretability and lessen overfitting. The CICIDS-2018 dataset has a lot of classes,
therefore choosing the best features is not an easy task. The techniques used to choose the ideal
feature set, the tests that were run, and the features that were adjusted or removed before feature
analysis are covered in this section.
The most popular feature selection technique is Information Gain, a filter-based feature selection
technique [28]. Information Gain ranks characteristics and reduces noise caused by unimportant
features by identifying the characteristic that best communicates the most knowledge about a
certain class. When determining which feature will provide the most information, entropy, a
measure of uncertainty that describes the distribution of features, is calculated[29].
Information gain is a popular approach for feature selection, which involves identifying the most
relevant features in a dataset that can help to make accurate predictions. Here are the steps to
handle feature selection using information gain:
1. Compute the information gain for each feature: Information gain measures the reduction
in entropy (i.e., uncertainty) that results from splitting the data based on a particular
feature. Features with higher information gain are more useful for making predictions.
You can use a formula like the one below to calculate the information gain for each
feature:
𝐼𝑛𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛 𝐺𝑎𝑖𝑛 = 𝐸𝑛𝑡𝑟𝑜𝑝𝑦(𝑆) − ∑
|𝑆𝑣|
|𝑆|
𝐸𝑛𝑡𝑟𝑜𝑝𝑦(𝑆𝑣)
𝑉𝑎𝑙𝑢𝑒𝑠(𝐴)
(1)
where:
 S is the entire dataset
 Sv is the subset of S for which the feature value is v
 |S| is the total number of instances in S
 |Sv| is the number of instances in Sv
2. Rank the features based on their information gain: Once you have calculated the
information gain for each feature, you can rank them in descending order based on their
information gain values. The features with the highest information gain are the most
relevant and should be selected for the model.
3. Select the top N features: Depending on the size and complexity of your dataset, you may
want to select only the top N features with the highest information gain. This will help to
reduce the dimensionality of your data and improve the efficiency of your model.

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4. Train the model with the selected features: Once you have identified the top features
using information gain, you can train your model using only those features. This will
help to improve the accuracy and interpretability of your model.
Overall, using information gain for feature selection can help to improve the performance and
efficiency of your machine learning models by reducing the dimensionality of your data and
identifying the most relevant features.
3.4. Class Imbalance
Class imbalance is a crucial factor to take into account in cybersecurity and machine learning.
The researchers' top aim is to increase detection precision. Thus, it is not a good idea to use
accuracy as the only statistic if the dataset is uneven and dominated by a single category. The
efficacy of the system implies that this unbalanced structure requires development. Random
oversampling and the synthetic minority oversampling approach (SMOTE), which frequently
results in a low rate of anomaly detection, are used to address the issue of class-imbalanced
data[30], [31], and may be employed to produce more data in minority classes where there is a
dearth of information. This can then be used to create the matrices that can be used to calculate
the unbalanced ratio[32]. Where the data size for class I is shown by Xi. The ratio between the
maximum and minimum instances of each class is, in other words, the imbalance ratio. Thus,
system efficiency should be increased by lowering this imbalance rate. When one group of
individuals is overrepresented in comparison to another, there is a class imbalance.The
imbalanced classification problem is caused, numerically, by the ratio of benign traffic to all
traffic. Table 1 demonstrates that Benign accounts for a sizeable portion of the data with
13,484,708 records, or 83% of the total, while each type of assault accounted for less than 5% of
the records, or roughly three million records, or 17% of all records, while Benign accounts for
13,484,708 records, or 83% of the total
Table 1. CICIDS-2018 data distribution [22]
Class Type Number Volume (%)
Benign 13,484,708 83.0700
DDOS attack-HOIC 686,012 4.2260
DDoS attacks-LOIC-HTTP 576,191 3.5495
DoS attacks-Hulk 461,912 2.8455
Bot 286,191 1.7630
FTP-BruteForce 193,360 1.1912
SSH-Bruteforce 187,589 1.1556
Infilteration 161,934 0.9976
DoS attacks-SlowHTTPTest 139,890 0.8618
DoS attacks-GoldenEye 41,508 0.2557
DoS attacks-Slowloris 10,990 0.0677
DDOS attack-LOIC-UDP 1,730 0.0107
Brute Force -Web 611 0.0038
Brute Force -XSS 230 0.0014
SQL Injection 87 0.0005
Total 16,232,943 100
From Table 1 generates a pie chart to display the proportion of bening and attack traffic in a
dataset as Figure 2.

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Figure 2. Proportion of Benign and Attacks
3.5. Classifiers
To determine which classifier performs the best, the training model is constructed and fitted with
various classifiers using the model.fit() method. The quality of the learning model and dataset has
a significant impact on an IDS's effectiveness [33]. Predicting the class of a given dataset is a step
in the classification process. Based on whether the traffic is malicious or benign, binary and
multiclass assaults are categorized inside IDS. Binary classification makes use of two clusters,
whereas multiclass classification expands the notion to include "n" clusters, allowing for
prediction of numerous categories or classes. Given that there are more classifications, multiclass
classification is frequently more difficult than binary classification. As a result, algorithms must
exert more effort and take longer to complete jobs, which could result in less effective results[6].
Each dataset needs to be analyzed, categorized as normal or aberrant, and the present structures
saved for future use. Although abuse detection and anomaly detection are both possible
applications of classification, the latter is more frequently used. This study handled feature
selection and class imbalance using five machine learning algorithms. This is a more detailed
explanation of these.
3.5.1. Random Forest classifier (RF)
Using the outputs of several decision trees, each of which was applied to a different subset of a
dataset, a machine learning classifier called Random Forest (RF) improves prediction accuracy. It
is comparable to the bootstrapping process used in the CART decision tree model. Using
different samples and initial parameters, RF tries to construct many CART models. The final
forecast, which consists of a substantial number of decision trees that each function
independently to anticipate the class outcome, is based on the class that receives the majority of
votes [9]. Random Forest include fewer control and model parameters than other models, a lower
error rate, resistance to overfitting, and the ability to employ a broad variety of potential attributes
without having to pick features. Also, when the number of trees in the forest increases, the
variance of the model decreases but the bias remains constant.
3.5.2. XGBoost
XGBoost is a machine learning software that uses gradient-boosted decision trees and is
primarily concerned with performance and speed. It is an effective tool for maximizing the

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hardware and memory resources available for tree boosting algorithms, allowing algorithm
refinement, model modification, and deployment in computer systems. The three main gradient
boosting methods—gradient boosting, regularized boosting, and stochastic boosting—are
supported by XGBoost. Several applications prefer the technique since it considerably reduces
computation time and increases memory consumption[34].
3.5.3. Decision Tree (DT)
A machine learning approach called a decision tree creates a model that resembles a tree and
describes the link between attributes and a class label. It divides observations recursively based
on the property with the highest gain ratio value that is the most informative. Although
continuous data can also be handled by transforming it to categorical data, decision trees are best
suited for data sets with categorical data. As DT models are provided as a set of rules, one benefit
is that they are easy to interpret. Each non-leaf node represents a test on a feature attribute, and
each branch shows the outcome of this feature attribute on a certain value domain. In order to
classify an item using a DT, the associated feature attribute must first be tested. Next, the output
branch must be chosen based on its value until it reaches the leaf node, where the category stored
there is used as the decision outcome[35]. A category is stored in each leaf node.
3.5.4. Extremely Trees Classifier
The Extra Trees Classifier (ETC) machine learning algorithm is a member of the ensemble
method family. It is similar to the Random Forest algorithm but chooses split points in the
decision trees in a different way. Using a variety of randomly selected feature and data subsets,
ETC builds numerous decision trees, and each tree casts a vote for the final classification
outcome. Unlike Random Forest, ETC chooses split points at random, disregarding the ideal split
point. [35]. ETC is faster than other decision tree-based models thanks to this method, which also
makes it less prone to overfitting. By giving the minority class samples more weight during the
training process, ETC is also better able to manage imbalanced datasets than other methods.
Overall, ETC is a strong and effective classification method that may be applied to a variety of
tasks, such as intrusion and anomaly detection.
3.5.5. Ensemble Approach
An ensemble learner is a machine learning technique that combines multiple individual models to
improve the accuracy and robustness of the overall prediction. The individual models can be of
different types, using different algorithms or feature sets, and are trained independently on the
same or different datasets. The ensemble learner then combines the predictions of the individual
models using a voting or weighted average method to make the final prediction. Ensemble
learners are often used in classification problems and can be categorized into two main types:
bagging and boosting. Bagging involves training each model on a random subset of the training
data, while boosting focuses on training each model on the examples that were previously
misclassified by the ensemble. Ensemble learning has been shown to be highly effective in
improving the performance of machine learning models, especially when the individual models
have different biases or error patterns.
IDSs have been demonstrated to perform better when using ensemble techniques, especially
when spotting uncommon and unknown assaults. Also, they can increase the effectiveness of the
detection process and lower the likelihood of false alarms. However, compared to single
classifiers, ensemble approaches might be more computationally expensive and resource-
intensive. As a result, the characteristics of the dataset and the resources at hand should be taken
into consideration while choosing an ensemble approach.

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3.6. Evaluation Metrics
The classification report is a graphical depiction that shows the four key classification model
parameters, Precision, Recall, F1-score, and Support. These values are used to gauge the
correctness of the model fitting. By including numerical scores for convenience, it makes
interpretation and detection simpler. True Positive (TP), True Negative (TN), False Positive (FP),
and False Negative are the four results of Confusion Matrix (FN)
3.6.1. Accuracy
An essential performance statistic, accuracy shows how well a classification model, or classifier,
can accurately predict previously unknown data. It stands for the model's capacity for accurate
prediction. Precision equals TP+TN/TP+FP+FN+TN.
3.6.2. Precision
A crucial performance criterion that needs to be considered is precision. The ratio of correctly
observed positive findings to all observed positive results is what is gauged. Precision equals
TP/TP + FP.
3.6.3. Recall
The recall is determined by dividing the total number of observations in a class by the proportion
of accurately observed positive findings. The proportion of positive observations is represented
by its output. TP/TP + FN = recall.
3.6.4. F1-Score
The F1-score, which is more important than accuracy, is a critical performance metric to take into
account. The costs of false positives and false negatives might not be comparable when working
with a large dataset. Accuracy might not be the best choice when expenses are not equal. In these
situations, the F1-score needs to be looked at for a more precise assessment.
The F1-Score is calculated as 2 * (Precision * Recall)/(Precision + Recall).
4. EXPERIMENTAL SETUPS AND RESULTS
With an IMac Pro with an Intel Xeon W 3.2 GHz (8-Cores), 32 GB of 2666 MHz DDR4
Memory, and a 1 TB HDD, all experiments are conducted. The scripts were created using the
numpy, pandas, and sklearn libraries in the Python (Version 3.9) environment.
SMOTE is a data augmentation technique that involves synthesizing new data points for the
minority class by interpolating between existing data points. Here are the steps to apply SMOTE
to the training data:
1. Identify the minority class: In the case of the CIC-IDS-2018 dataset, the minority class is
the malicious traffic class.
2. Calculate the imbalance ratio: Calculate the imbalance ratio between the minority and
majority classes in the training data. This will help to determine the number of synthetic
data points to be generated by SMOTE.

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3. Apply SMOTE: Use a library such as imblearn to apply SMOTE to the training data. The
SMOTE function takes as input the training data and the imbalance ratio and generates
new data points for the minority class. The number of synthetic data points generated by
SMOTE should be proportional to the imbalance ratio. For example, if the imbalance
ratio is 1:10 (i.e., the minority class has 10% of the samples of the majority class),
SMOTE should generate 9 new data points for each existing data point in the minority
class.
4. Combine the original and synthetic data: Combine the original training data with the
synthetic data generated by SMOTE to create a new balanced training data set.
5. Shuffle the data: Shuffle the new balanced training data set to avoid any bias in the order
of the data points.
6. Train the model: Train a classification model on the new balanced training data set.
By applying SMOTE to the training data, you can increase the number of samples in the minority
class and balance the class distribution, which can lead to better performance of the machine
learning model. It's important to note that while SMOTE can help to address class imbalance, it
may not always lead to the best performance and other techniques may need to be considered.
The imblearn library provides a range of functions for handling imbalanced datasets, including
the SMOTE function. Here are the steps to apply SMOTE to the training data using imblearn:
1. Import the necessary libraries: Start by importing the necessary libraries. You will need
the imblearn library for applying SMOTE and the NumPy library for data manipulation.
from imblearn.over_sampling import SMOTE
import numpy as np
2. Create the SMOTE object: Create an instance of the SMOTE class, which will be used to
apply the SMOTE algorithm to the training data. You can specify the sampling strategy
as "minority" to only apply SMOTE to the minority class.
smote = SMOTE(sampling_strategy='minority')
3. Fit and transform the training data: Apply the fit_transform method of the SMOTE object
to the training data to generate synthetic data points for the minority class. This method
takes as input the feature matrix X_train and the target vector y_train and returns the
balanced training data set.
X_train_balanced, y_train_balanced = smote.fit_transform(X_train, y_train)
4. Check the class distribution: Verify that the class distribution of the balanced training
data set is now balanced by calculating the number of samples in each class.
The SMOTE function generates synthetic data points for the minority class by interpolating
between existing data points. The number of synthetic data points generated for each existing
data point is proportional to the imbalance ratio between the minority and majority classes. For
example, if the imbalance ratio is 1:10 (i.e., the minority class has 10% of the samples of the
majority class), SMOTE should generate 9 new data points for each existing data point in the
minority class.

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By applying SMOTE to the training data using the imblearn library, it can generate synthetic data
points for the minority class and balance the class distribution, which can improve the
performance of the machine learning model.
We obtained the results presented in Figure 3, which displays the imbalanced dataset before the
classification stage. It’s showing the distribution of the classes in dataset, with the bars colored
blue for the “Benign” class (label 0) and red for the “Attacks” class (label 1).
Figure 3. The Distribution of the classes after imbalanced
The results represent the importance values of 19 different features obtained through feature
selection using the Information Gain method. Information Gain measures the amount of
information provided by a feature to the classification task. The greater the Information Gain, the
more significant the feature becomes for classification. Based on the findings, here are the top 5
features exhibiting the highest Information Gain:
1. Init Fwd Win Byts (Information Gain = 0.746076591844705)
2. Flow IAT Max (Information Gain = 0.6545814589068368)
3. Flow Duration (Information Gain = 0.6405164250112803)
4. Fwd Pkts/s (Information Gain = 0.634605317927722)
5. Bwd Pkt Len Min (Information Gain = 0.6316676523933165)
The remaining features have Information Gain values ranging from 0.529 to 0.597.Based on
these results, it may be beneficial to focus on the top 5 features during further analysis and
modeling. These features appear to have the most significant impact on the classification task,
and using them could potentially result in a more accurate and efficient model. However, it is
essential to note that the importance of features can vary depending on the specific dataset and
classification task. Therefore, it is important to evaluate and validate the results of feature
selection thoroughly as shown in Table 2.

105
Table 2. Important feature with value
No. Feature Importance Value
1 Init Fwd Win Byts 0.7460765918447050
2 Flow IAT Max 0.6545814589068368
3 Flow Duration 0.6405164250112803
4 Fwd Pkts/s 0.6346053179277220
5 Bwd Pkt Len Min 0.6316676523933165
6 Flow IAT Mean 0.6227957388844962
7 Flow Pkts/s 0.6206618556552554
8 Fwd IAT Max 0.6193223696107817
9 Fwd IAT Tot 0.6077872354594660
10 Fwd IAT Mean 0.5973932520832026
11 Fwd Header Len 0.5359267744506864
12 Subflow Fwd Byts 0.5355080035978141
13 Pkt Len Max 0.5342673508275444
14 Fwd Seg Size Avg 0.5330819927752828
15 Fwd Pkt Len Mean 0.5308392160721118
16 Pkt Len Mean 0.5304229611917282
17 Dst Port 0.5293337733399603
18 Pkt Size Avg 0.5283890303812000
19 Fwd Pkt Len Max 0.5280293285443132
The results in Table 3 represent the performance of five different classifiers: Decision Tree,
Random Forest, Extra Tree, XGBoost, and an Ensemble model. The evaluation metrics used to
measure the performance of each classifier are Accuracy, Precision, Recall, and F1-Score.
Table 3. Performance of different classifiers.
Classifiers Accuracy Precision Recall F1-Score
Decision Tree 0.9786 0.9808 0.9786 0.9779
Random Forest 0.9831 0.9822 0.9832 0.9796
Extra Tree 0.9819 0.9815 0.9819 0.9797
XGBoost 0.9334 0.9838 0.9834 0.9787
Ensemble 0.9836 0.9822 0.9836 0.9798
Accuracy measures the proportion of correctly classified instances out of the total instances. In
this case, the Random Forest classifier achieved the highest accuracy of 0.9831, followed closely
by the Ensemble classifier with an accuracy of 0.9836. The Decision Tree, Extra Tree, and
XGBoost classifiers had accuracies of 0.9786, 0.9819, and 0.9334, respectively. Precision
measures the proportion of true positives (correctly predicted positive instances) out of all
positive predictions. The Decision Tree, Random Forest, Extra Tree, and Ensemble classifiers
achieved high precision scores of 0.9808, 0.9822, 0.9815, and 0.9822, respectively. The XGBoost
classifier had the highest precision score of 0.9838.Recall measures the proportion of true
positives out of all actual positive instances. The Random Forest and Ensemble classifiers
achieved the highest recall scores of 0.9832 and 0.9836, respectively. The Decision Tree and
Extra Tree classifiers had recall scores of 0.9786 and 0.9819, respectively. The XGBoost
classifier had a recall score of 0.9834.F1-Score is the harmonic mean of precision and recall and
provides a single metric that combines both measures. The Random Forest and Ensemble
classifiers achieved the highest F1-Scores of 0.9796 and 0.9798, respectively. The Decision Tree,
Extra Tree, and XGBoost classifiers had F1-Scores of 0.9779, 0.9797, and 0.9787, respectively.

106
Overall, the results suggest that the Ensemble classifiers performed the best in terms of accuracy
and F1-Score. However, depending on the specific use case, other classifiers with high precision
or recall scores may be more appropriate. Additionally, further analysis may be needed to
determine the significance of any differences in performance between the classifiers.
Table 4. Performance of classifiers with and without feature selection.
Classifiers Accuracy
(with FS)
Accuracy
(with FS)
F1-Score
(with FS)
F1-Score
(without FS)
Decision Tree 0.9786 0.9575 0.9779 0.9476
Random Forest 0.9831 0.9737 0.9796 0.9611
Extra Tree 0.9819 0.9687 0.9797 0.9535
XGBoost 0.9334 0.9266 0.9787 0.9118
Ensemble 0.9836 0.9739 0.9798 0.9614
The results (Table 4) show that feature selection improved the performance of all classifiers in
terms of accuracy and F1-Score. In particular, the Random Forest and Ensemble classifiers
achieved a higher accuracy and F1-Score with feature selection. For example, the Random Forest
classifier achieved an accuracy of 0.9831 and an F1-Score of 0.9796 with feature selection,
compared to an accuracy of 0.9737 and an F1-Score of 0.9611 without feature selection.
These results demonstrate the effectiveness of the proposed feature selection technique in
improving the performance of the classifiers. By selecting the most relevant features, the models
were able to achieve higher accuracy and F1-Scores while maintaining high precision and recall.
This emphasizes the significance of feature engineering in machine learning and underscores the
potential advantages of thoughtfully choosing features to enhance model performance.
The study on anomaly detection through feature selection is similar to the other studies in that it
compares the performance of multiple classifiers to identify the most suitable one for a specific
task. However, this study has a specific focus on the impact of feature selection on model
performance, and it emphasizes the importance of feature engineering in machine learning. One
unique aspect of this study is its focus on an imbalanced dataset, which is a common problem in
machine learning. The study shows that classifiers can achieve high accuracy and F1-Scores even
in the presence of class imbalance, indicating their robustness. However, the specific use case
may require a classifier with higher precision or recall, and further experimentation may be
needed to identify the best classifier for a given application.
Overall, the study highlights the importance of feature selection in improving model
performance, and it demonstrates the effectiveness of five classifiers in an anomaly detection
task. The study's findings can be valuable for practical applications of machine learning, where
model performance is crucial for identifying anomalies and making accurate predictions.
6. CONCLUSIONS
By experimentation, this study sought to determine how feature selection can increase the
accuracy of anomaly detection. The Information Gain technique was used since it can determine
how much weight to give to feature information. Using Accuracy, Precision, Recall, and F1-
Score criteria, the performance of five classifiers Decision Tree, Random Forest, Extra Tree,
XGBoost, and Ensemble model was assessed. The Ensemble classifier came in second with an
accuracy of 0.9836, closely behind the Random Forest classifier, which had the greatest accuracy
of 0.9831. However, it is worth noting that the dataset used in this study was imbalanced, with
the majority class representing over 98% of the instances, which can present challenges for

107
classification models. Despite this, the classifiers achieved high accuracy and F1-Scores,
indicating their robustness to class imbalance.
Furthermore, the study showed that feature selection played a crucial role in the performance of
the classifiers. By selecting the most relevant features, the models achieved higher accuracy and
F1-Scores while maintaining high precision and recall. This emphasizes the significance of
feature engineering in machine learning and the possible benefits of selecting features properly to
maximize model performance.
In summary, this study demonstrated the performance of five classifiers on an imbalanced dataset
with carefully selected features. The Random Forest and Ensemble classifiers performed the best
in terms of accuracy and F1-Score, but the specific use case may require a classifier with higher
precision or recall. Further analysis and experimentation may be needed to determine the best
classifier for a particular application, but the results underscore the importance of feature
selection and its potential to improve model performance.
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AUTHORS
Witcha Chimphlee his PhD in Computer Science from the University Technology of
Malaysia. Currently, he holds the position of Assistant Professor in the Data Science and
Analytics department at Suan Dusit University in Thailand. His current research interests
include Machine learning, computer networks and security, data science, and big data
analytics. He has published several papers in peer-reviewed journals and has actively
participated in various international conferences. He is a dedicated researcher with a passion for advancing
the field of computer science through his work
Siriporn Chimphlee, holds a PhD in Computer Science from the University Technology
of Malaysia and is currently an Assistant Professor in the Data Science and Analytics
department at Suan Dusit University in Thailand. Her research interests include data
mining, intrusion detection, web mining, and information technology. She has published
several papers in reputed journals and has actively participated in international
conferences. She is passionate about exploring new avenues in computer science and
constantly strives to contribute to the field through her research.

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