An ensemble-based approach for effective distributed denial of service attack detection in software defined networking

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 13, No. 2, June 2024, pp. 2019~2026
ISSN: 2252-8938, DOI: 10.11591/ijai.v13.i2.pp2019-2026  2019
Journal homepage: http://ijai.iaescore.com
An ensemble-based approach for effective distributed denial of
service attack detection in software defined networking
Mohammed Majid Ahmed1
, Hasan Abdulkader2
1
Department of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey
2
Department of Software Engineering, Faculty of Engineering, Haliç University, Istanbul, Turkey
Article Info ABSTRACT
Article history:
Received Jul 23, 2023
Revised Nov 2, 2023
Accepted Dec 2, 2023
Software defined networking (SDN) is a network framework that aims to
redefine network characteristics through the programmability of network
components, faster and larger network monitoring, centralized network
operation, and effective detection of fraudulent traffic and special
malfunctions. However, SDN networks are vulnerable to security threats that
can cause complete network failure. To address this issue, in this paper,
machine learning techniques are suggested for the swift detection of attacks.
Various methods for detecting distributed denial of service (DDoS) attacks
are evaluated, and the study identifies the most precise method for
categorizing such attacks within a SDN network. The results indicate that the
proposed system achieves high accuracy in detecting DDoS attacks, with
ensemble learning achieving 99% accuracy. This indicates a remarkable
improvement percentage in comparison to the approaches of decision tree
(DT), k-nearest neighbors (KNN), and support vector machine (SVM).
Keywords:
Distributed denial of service
attack
Ensemble learning
Machine learning
Software defined radio
This is an open access article under the CC BY-SA license.
Corresponding Author:
Hasan Abdulkader
Department of Software Engineering, Faculty of Engineering, Haliç University
Istanbul, Turkey
Email: hasanabdulkader@halic.edu.tr, hasan.abdulkader@yahoo.com
1. INTRODUCTION
With the rapid increase in internet availability, the need for enhanced security measures has become
more crucial compared to the past. Among the significant challenges is the accurate detection of distributed
denial of service (DDoS) malicious flows to prevent resource failures caused by discarding these malicious
flows [1]. The current mitigation strategies rely on appliances, including firewalls [2], but these approaches
utilize predefined security policies that often fall short in detecting and classifying novel attack patterns and
responding appropriately [3]. Software defined networking (SDN) has emerged as a promising technique for
mitigating DDoS attacks by offering centralized control and programmability.
The effectiveness of SDN-based DDoS detection and mitigation methods largely hinges on the
accuracy of their underlying detection models [4]. Ensemble classification, which combines the outputs of
multiple classifiers to improve accuracy, has shown potential in various domains, including network intrusion
detection [5]. This study proposes an ensemble classification method to enhance the accuracy of DDoS attack
detection in SDN.
SDN is a technology that enhances network management by allowing programmable network
configuration to optimize performance. SDN is structured into three layers: the application layer, the control
layer, and the data plane. The application layer communicates its requirements for network resources to the
control layer through application programming interfaces (API), while the control layer communicates with
the data plane through south bound API. SDN separates network packets from the routing process, allowing

 ISSN: 2252-8938
Int J Artif Intell, Vol. 13, No. 2, June 2024: 2019-2026
2020
for centralized intelligence in the control layer [6], [7]. This layer can have one or more controllers and is the
brain of the SDN network. Centralized traditional static network architecture, SDN offers greater flexibility
and easier troubleshooting, but its centralized architecture presents challenges in security, elasticity, and
scalability [8].
DDoS attack detection has been studied merely in research and academic references. There have
been numerous proposals to address DDoS attacks in networks, which have been organized into different
categories. The proposed methods can be broadly divided into two types: those that rely on machine learning
and those that are based on statistics [9], [10].
A DDoS attack involves flooding a network with malicious traffic, overwhelming the resources such
as servers and bandwidth, and rendering the network inaccessible. As per the 2020 annual internet report by
Cisco, it is projected that the global count of DDoS attacks will increase twofold, reaching 15.4 million by
the year 2023 [11]. The report emphasizes the need to focus on preventing DDoS attacks, which have caused
denial of service (DoS) in major companies like CNN, Netflix, and Twitter in the past [12]. The main
objective of a security operation center (SOC) is to effectively identify and eliminate malicious DDoS flows,
ensuring the protection of network resources and maintaining service availability. This research paper
specifically concentrates on enhancing the precision of DDoS flow detection, which will be implemented
within the centralized SDN controller, whether it is physical or conceptual in nature.
Dong and Sarem [13] created a network structure to initiate a DDoS attack on a host, consisting of
one server and ten clients for network connectivity. They employed an enhanced k-nearest neighbors (KNN)
algorithm to uncover the DDoS attack. These algorithms yielded a favorable prediction rate of 0.912 each.
Indexing and abstracting services depend on the accuracy of the title, extracting from it keywords useful in
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which it was intended, so be specific.
Diaz et al. [14] devised a structure to identify and mitigate low-rate distributed denial-of-service
(LR-DDoS) attacks in SDN. Within this framework, a machine learning-based intrusion detection system
(IDS) has been incorporated, which exhibits a remarkable detection rate of 95%. The performance of the
architecture was assessed in a simulated environment that closely resembles actual production networks. As a
result, the IDS effectively thwarted all detected attacks.
Polat et al. [15] presented a model to detect DDoS attacks using machine learning techniques
including support vector machine (SVM), KNN, Naïve Bayes, and artificial neural networks (ANN). Two
datasets of SDN were utilized, containing instances of normal network conditions and instances of DDoS
attacks, in order to capture their distinct characteristics. Additionally, a new dataset was generated by
applying a feature selection approach to the existing dataset. The results of the study showed that the KNN
classifier achieved an accuracy rate of 98.3%, surpassing the efficiency of previous models.
Rahman et al. [16] conducted a study where they utilized various machine learning algorithms,
including J48, random forest, SVM, and KNN, to detect and prevent DDoS attacks in SDN network. They
incorporated a script to assist in the mitigation and reduction of attacks and evaluated multiple models to
identify the most suitable one for the proposed network. The findings demonstrated that J48 exhibited
superior performance compared to the other algorithms, particularly in terms of training and testing time.
Nadeem et al. [17] conducted a study to compare different machine learning classifiers, such as
KNN, Naïve Bayes, random forest, decision trees (DT), and SVM. The classifiers were assessed in terms of
their accuracy, precision, recall, and specificity. The findings revealed that the random forest classifier, with
feature subset elimination, outperformed the other algorithms in detecting DDoS flood attacks in the context
of SDN.
Meti et al. [18] conducted a machine learning algorithm comprising Naïve Bayes, neural networks,
and SVM. Machine learning models were introduced to detect DDoS flood attacks in SDN. The proposed
algorithm achieved accuracies of 70% for Naïve Bayes, 80% for neural networks, and 80% for SVM.
Ahmad et al. [19] proposed the use of machine learning techniques for mitigating SDN DoS and
DDoS attacks. Their objective was to derive significant insights by evaluating machine learning algorithms
for security detection in forthcoming communication networks. Furthermore, they assessed these approaches
based on their influence on the controller during DDoS attacks. The research revealed that SVM achieved an
impressive accuracy rate of 97.5%.
Tan et al. [20] proposed a method for detecting and mitigating SDN DDoS attacks. Their approach
involved monitoring the network to identify unexpected flows by utilizing a detection state specifically
designed for DDoS on the data layer. They employed machine learning techniques such as k-means and
KNN algorithms to detect abnormal flows based on the detection trigger mechanism and rate asymmetry
characteristics of the streams. Subsequently, the controller implemented appropriate countermeasures to
respond to the attacks. The authors successfully enhanced the accuracy and efficiency of detection while
mitigating SDN threats by introducing a novel framework that combines control plane and data plane

Int J Artif Intell ISSN: 2252-8938 
An ensemble-based approach for effective distributed denial of service attack ... (Mohammed Majid Ahmed)
2021
cooperative detection techniques. In this paper suggests enhancing the security of DDoS attack detection in
SDN controller by utilizing ensemble learning machine learning techniques.
This paper contributes to research on DDoS attacks detection using an innovative approach of
ensemble learning. The composition of ensemble learning namely random forest, gradient boosting, and
logistic regression has been tested and optimized to deliver high performance proved by computer
simulation. Also, the paper made use of a recent dataset [21] published in 2020 issue from a network
simulation using Mininet emulating ten topologies made up with switches connected to a unique controller.
23 features collected for the sake of SDN network traffic classification into benign and malicious patterns. In
our work we analyze the importance of all features, including alphabetic and structured features after
converting them into numeric format. This step reveals that some converted features have significantly much
more vital than less important features in the original dataset. Our research continues by selecting the 5 most
important features to leverage the problem of DDoS attacks classification.
The paper is structured into five sections: section 1 is the present introduction. Details of the dataset
processing, machine learning models, and the research methodology are developed in section 2. Section 3
presents the results and discussion of founding. Finally, section 4 concludes the paper.
2. METHOD
This section will cover the proposed model in four subsections, which are proposed approach,
dataset, feature extraction, selection, and machine learning models. The first subsection explains the
proposed methodology consisting in using ensemble of machine learning such as a powerful tool to leverage
complex problems. Machine learning models simulated in this research are presented in subsections 2.1 and
2.4. Subsections 2.2 and 2.3 develop the dataset attributes and features characteristics.
2.1. Proposed method (ensemble learning)
Protecting against DDoS attacks is of utmost importance in the context of SDN due to its centralized
controller architecture. To effectively combat the constantly evolving nature of these attacks, it is essential to
employ updated and innovative systems. This is where the evaluation of machine learning methods for DDoS
detection becomes crucial. By training the detection system to recognize traffic patterns based on new
information, machine learning offers a highly accurate and efficient solution compared to other detection
methods. There are three primary categories for DDoS detection: information-theory based detection,
machine learning-based detection, and ANN-based detection. In this study, machine learning-based detection
was chosen due to its ease of implementation and relatively high precision compared to information-theory
based models. The widely used DDoS attack SDN dataset was utilized to train the machine learning models.
The trained models were then employed to predict whether the network data was anomalous or benign.
This research paper introduces ensemble learning to enhance the precision of detecting DDoS flows.
To differentiate DDoS flows, the study utilizes three classification algorithms: DT, KNN, and SVM. The
research proposes an ensemble learning method that combines three algorithms (gradient boosting, random
forest, and logistic regression) to improve the accuracy of classification. The outcomes from each classifier
are aggregated using a voter mechanism (soft voting). The DDoS attack SDN dataset, designed specifically
for SDN, is employed in this project and was generated using the Mininet emulator. The dataset is divided
into two sets, with 70% allocated for training and 30% for testing purposes. This concept is visually
represented in Figure 1.
The model put forth in this study precisely differentiates between legitimate flows and DDoS flows.
The results of the study illustrate this ability of the model. Moreover, the accuracy, precision, recall, and
F-score of the model is evaluated in comparison to other models. These statistical metrics will be detailed in
section 3. The proposed method in this paper uses an ensemble learning of machine learning consisting of
gradient boosting classifier, random forest classifier, and k-neighbors classifier. The ensemble method
combines the predictions of these classifiers using soft voting, which considers the predicted probabilities of
each classifier rather than just the majority vote. The proposed model is depicted in Figure 2.
2.2. Dataset
The proposed approach made use of the DDoS attack SDN dataset, which had been collected by
Bennett University. The dataset had been specifically designed for SDN and was generated using the Mininet
emulator. It fulfilled the objective of traffic categorization, utilizing machine learning and deep learning
algorithms. The simulation, various types of traffic were generated, including legitimate transmission control
protocol (TCP), user datagram protocol (UDP), and internet control message protocol (ICMP) traffic. In
addition to malicious TCP Syn attacks, UDP flood attacks, and ICMP attacks were also included. The
dataset, as depicted in Table 1, consisted of 23 attributes [21].

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Figure 1. The architecture of the proposed method
Figure 2. The suggested ensemble learning method for detecting DDoS attacks
Table 1. Main features in the dataset
No. Features No. Features
1. dt 13. byteperflow
2. switch 14. pktrate
3. src 15. Pairflow
4. dst 16. Protocol
5. pktcount 17. port_no
6. bytecount 18. tx_bytes
7. dur 19. rx_bytes
8. dur_nsec 20. tx_kbps
9. tot_dur 21. rx_kbps
10. flows 22. tot_kbps
11. pktperflow 23. Label
12. Packet_ins

2023
2.3. Feature extraction and selection
As mentioned, the dataset used in this study has 23 features that can be used for classification,
however, not all of them are equally important. Including a high number of features can increase the
complexity of the model, which in turn can lead to overfitting. Overfitting transpires when the model
excessively conforms to the training data, resulting in subpar performance when presented with new data. To
mitigate this, the analysis focused on the five most critical features, namely 'byteperflow', 'pktperflow',
'bytecount', 'pktrate', and 'pktcount'. These features, along with their corresponding details, are outlined in
Table 2 and Figure 3.
Table 2. Features selection
Features Description
'byteperflow' The average number of bytes per flow in the dataset.
'pktperflow' Average number of packets per flow in the dataset.
'bytecount' Total count of bytes across all flows in the dataset.
'pktrate' Packet rate, which refers to the number of packets transmitted or received per unit of time.
'pktcount' Total count of packets across all flows in the dataset.
Figure 3. Top features of the dataset
2.4. Other machine learning models
In literature, a huge number of machine learning exists, and they have been applied in a variety of
applications. Classification is an important field of study where machine learning excels and show good
performance. In this subsection we focus on the classification algorithms DT, KNN, and SVM.
2.4.1. Decision trees
DT are known for their simplicity and remarkable accuracy in classification tasks. The algorithm
functions by dividing the data recursively, using selected features, to create subsets that are more easily
classified due to their homogeneity. At each internal node, a decision is made based on the value of a feature,
dictating the branch to follow. This iterative process persists until a leaf node is reached, ultimately providing
the final classification. The appeal and effectiveness of DT lie in their simplicity, interpretability, and capacity
to handle diverse data types, all of which contribute to their widespread adoption and ability to achieve high
accuracy in classification [22]. The ultimate classification is established by examining various attributes. In the
DT algorithm, the Gini index is employed to perform the classification. Gini index is formulated as (1):
𝐺𝑖𝑛𝑖 = 1 − ∑ (𝑝𝑖)2
𝑛
𝑖=1
(1)
In (1), 𝑝𝑖 is the probability of an object being classified to a particular class.
2.4.2. K-nearest neighbors
KNN is a supervised machine learning classifier that handles both classification and regression
problems. It determines the class or value for new data by considering its KNN. In order to determine the
separation between two data points, different distance functions such as Hamming, Manhattan, and Euclidean
are utilized. The Euclidean distance function calculates the distance between the incoming data and the data
already present in the training set. Based on the KNN, a selection is made from the classified set. The
mathematical expression for Minkowski distance can be defined as (2):

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𝑑(𝑥, 𝑦) = (∑ |𝑥𝑟 − 𝑧𝑟|𝑝
𝑑
𝑟=1 )
1
𝑝
⁄
(2)
where 𝑑(𝑥, 𝑦) represents the Minkowski distance between two points 𝑥 and 𝑦, xi and yi denote the values of
the i-th feature (or dimension) of points 𝑥 and 𝑦, respectively and 𝑝 is a parameter that controls the degree of
the Minkowski distance. It is a positive real number.
2.4.3. Support vector machine
The SVM is a binary classifier in machine learning. It aims to create accurate decision boundaries to
categorize data. These boundaries, known as hyper-planes, are constructed using unique support vectors. In
classification, two classes are separated by a hyper-plane [23]. SVM can achieve high accuracy in classification but
has a longer training time compared to other classification algorithms. The implementation of the SVM model
involves utilizing a radial basis kernel function. In this approach, the optimal hyperplane is calculated as in (3):
𝑘(𝑥1, 𝑥2) = 𝑒𝑥𝑝 (−
‖𝑥1−𝑥2‖2
2𝜎2 ) (3)
‖𝑥1 − 𝑥2‖2
represents the squared Euclidean distance between the new data point x and the support vector xi
and γ is a hyperparameter that controls the width of the kernel and affects the smoothness of the decision
boundary. It is a positive real number.
3. RESULTS AND EVALUATION
The effectiveness of the machine learning classifier was evaluated using different metrics such as
accuracy, precision, recall, and F1 score. These metrics were assessed by constructing a confusion matrix
based on actual and estimated probabilities. The composition of the confusion matrix in a binary
classification case is given in Table 3. The mathematical formulas for calculating these metrics are as:
- True positive (TP) refers to the accurate identification of attack traffic as an attack.
- False positive (FP) indicates the erroneous detection of normal traffic as an attack.
- True negative (TN) represents the correct identification of normal traffic as normal.
- False negative (FN) denotes the misclassification of normal traffic as an attack.
Table 3. Confusion matrix composition
Actual class
Positive Negative
Predicted class
Positive TP FP
Negative FN TN
A variety of evaluation metrics, such as accuracy, precision, and recall, have been selected to
evaluate the effectiveness of machine learning classifiers. These metrics are calculated using a confusion
matrix. Assessing the performance of these classifiers is vital for accurately detecting attacks in the SDN
controller. The mathematical representation of these metrics is provided as (4) to (7):
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
𝑇𝑃+𝑇𝑁
𝑇𝑃+𝐹𝑁+𝑇𝑁+𝐹𝑃
(4)
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =
𝑇𝑃
𝑇𝑃+𝐹𝑃
(5)
𝑅𝑒𝑐𝑎𝑙𝑙 =
𝑇𝑃
𝑇𝑃+𝐹𝑁
(6)
𝐹1 =
2×𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛×𝑅𝑒𝑐𝑎𝑙𝑙
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+𝑅𝑒𝑐𝑎𝑙𝑙
(7)
Table 4 presents the simulation outcomes for each classifier. All metrics were evaluated in a
computer simulation after adequate training of the machine learning models. These results correspond to a
simulation conducted under the specified conditions described in this section. Based on the simulation
results, it is evident that the proposed model exhibits a high level of accuracy in detecting DDoS attacks,
surpassing 99%. This accuracy rate is exceptionally high, demonstrating the model's effectiveness.
Furthermore, the application of ensemble techniques further enhances accuracy, approaching nearly 100%.
This improvement in accuracy, pinpointed in Table 5, is particularly noteworthy and highlights the
significance of the boosting ensemble approach in enhancing the model's performance.

2025
Table 4. The results each classifier
Algorithm Precision (%) Recall (%) F1 score (%) Accuracy (%)
0 1 0 1 0 1 Training set Test set
DT 98 94 96 97 97 96 96.5 96.5
KNN 97 94 96 96 97 95 96 95.8
SVM 93 97 98 91 95 94 92.4 92.3
Logistic regression 72 67 83 52 77 58 71 70
Gradient boosting 98 92 95 97 96 95 95.4 95.5
Random forest 1.00 88 91 99 95 93 94.2 94.2
Ensemble learning 1.00 1.00 1.00 1.00 1.00 1.00 99.9 99.8
Table 5. The results of the proposed method compared to state of art
Reference Year Dataset Method name Accuracy (%)
[24] 2021 NSL-KDD dataset SVM-based IDS mechanism 95.98
[25] 2021 Real-Time dataset using
Floodlight controller and Mininet
SVM
DT
Logistic regression
Random forest
94.99
86.74
73
86
[26] 2023 KDD99 dataset Improved SVM algorithm 98.8
[27] 2022 KDD cup99 Logistic regression
KNN
DT
97.80
97.80
99
[28] 2023 Dataset from Kaggle Naïve Bayes
SVM
Naïve Bayes
70
80
96
Our proposed (ensemble learning) DDoS attack SDN dataset [21] Ensemble learning 99.8
4. CONCLUSION
The impact of DDoS attacks on networks is a significant concern, as they have the potential to cause
complete disruption if not effectively addressed. These attacks are growing in complexity and can easily
evade traditional protection techniques. To tackle network security challenges, machine learning techniques
are being implemented in SDN. The DT, KNN, and SVM algorithms are employed to construct implicit or
explicit models from available data. These models enable systems to learn from the data without explicit
programming, uncover hidden patterns, and gain valuable insights. By leveraging machine learning, it is
possible to enhance the effectiveness of network features, thereby contributing to the intelligent mitigation of
DDoS attacks. Among the employed machine learning algorithms, ensemble learning achieved the best
results with an accuracy of 99%, outperforming the other algorithms. In the future, there will be an emphasis
on creating a mitigation module specifically designed for the studied attacks in this research. Developing an
efficient and cost-effective mitigation plan entails tackling various obstacles. One such challenge involves
guaranteeing the termination of all dubious communications by leveraging SDN's programmability capability,
which can be achieved through the implementation of blocking rules in edge switches. Moreover, optimizing the
utilization of controllers and switching resources to implement mitigation policies, reducing the response time of
the mitigation system, and ensuring scalability of the solution are all important factors to consider in the process.
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BIOGRAPHIES OF AUTHORS
Mohammed Majid Ahmed received the bachelor’s degree in Computer
Engineering from Al-Hussain University College in 2016. He is Master student in Electrical
and Computer Engineering, Altinbas University 2021-2023. He can be contacted at email:
203720250@ogr.altinbas.edu.tr.
Dr. Hasan Abdulkader received a Ph.D. in Computer Science and
Telecommunications from the Institute National Polytechnique in Toulouse-France INPT. He
worked in several universities in France and abroad and he is now working with the Halic
University as an associate professor. He can be contacted at email:
hasan.abdulkader@yahoo.com.

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