Comparison of ML/DL Approaches for Detecting DDoS Attacks in SDN
Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/5/3033 |
| _version_ | 1797615699342917632 |
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| author | Tariq Emad Ali Yung-Wey Chong Selvakumar Manickam |
| author_facet | Tariq Emad Ali Yung-Wey Chong Selvakumar Manickam |
| author_sort | Tariq Emad Ali |
| collection | DOAJ |
| description | Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an SVM-based DDoS detection model shows superior performance. This comparative analysis offers a valuable insight into the development of efficient and accurate techniques for detecting DDoS attacks in SDN environments with less complexity and time. |
| first_indexed | 2024-03-11T07:30:23Z |
| format | Article |
| id | doaj.art-101b8a4cfaaa45ce8729e0239900909d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T07:30:23Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-101b8a4cfaaa45ce8729e0239900909d2023-11-17T07:18:23ZengMDPI AGApplied Sciences2076-34172023-02-01135303310.3390/app13053033Comparison of ML/DL Approaches for Detecting DDoS Attacks in SDNTariq Emad Ali0Yung-Wey Chong1Selvakumar Manickam2National Advanced IPv6 Centre, Universiti Sains Malaysia, Gelugor 11800, MalaysiaNational Advanced IPv6 Centre, Universiti Sains Malaysia, Gelugor 11800, MalaysiaNational Advanced IPv6 Centre, Universiti Sains Malaysia, Gelugor 11800, MalaysiaSoftware-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an SVM-based DDoS detection model shows superior performance. This comparative analysis offers a valuable insight into the development of efficient and accurate techniques for detecting DDoS attacks in SDN environments with less complexity and time.https://www.mdpi.com/2076-3417/13/5/3033SDNsupport vector machineK-nearest neighborsdecision treesmultiple layer perceptronconvolutional neural network |
| spellingShingle | Tariq Emad Ali Yung-Wey Chong Selvakumar Manickam Comparison of ML/DL Approaches for Detecting DDoS Attacks in SDN Applied Sciences SDN support vector machine K-nearest neighbors decision trees multiple layer perceptron convolutional neural network |
| title | Comparison of ML/DL Approaches for Detecting DDoS Attacks in SDN |
| title_full | Comparison of ML/DL Approaches for Detecting DDoS Attacks in SDN |
| title_fullStr | Comparison of ML/DL Approaches for Detecting DDoS Attacks in SDN |
| title_full_unstemmed | Comparison of ML/DL Approaches for Detecting DDoS Attacks in SDN |
| title_short | Comparison of ML/DL Approaches for Detecting DDoS Attacks in SDN |
| title_sort | comparison of ml dl approaches for detecting ddos attacks in sdn |
| topic | SDN support vector machine K-nearest neighbors decision trees multiple layer perceptron convolutional neural network |
| url | https://www.mdpi.com/2076-3417/13/5/3033 |
| work_keys_str_mv | AT tariqemadali comparisonofmldlapproachesfordetectingddosattacksinsdn AT yungweychong comparisonofmldlapproachesfordetectingddosattacksinsdn AT selvakumarmanickam comparisonofmldlapproachesfordetectingddosattacksinsdn |