Enhancing DDoS attack detection in IoT using PCA
Internet of Things (IoT) security and reliability rely on the capacity to identify distributed denial-of-service (DDoS) assaults in IoT networks. This research presents a comprehensive study on DDoS attack detection using the NSL-KDD dataset. The dataset contains a diverse set of network traffic dat...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-03-01
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| Series: | Egyptian Informatics Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866524000136 |
| _version_ | 1797248324717248512 |
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| author | Sanjit Kumar Dash Sweta Dash Satyajit Mahapatra Sachi Nandan Mohanty M. Ijaz Khan Mohamed Medani Sherzod Abdullaev Manish Gupta |
| author_facet | Sanjit Kumar Dash Sweta Dash Satyajit Mahapatra Sachi Nandan Mohanty M. Ijaz Khan Mohamed Medani Sherzod Abdullaev Manish Gupta |
| author_sort | Sanjit Kumar Dash |
| collection | DOAJ |
| description | Internet of Things (IoT) security and reliability rely on the capacity to identify distributed denial-of-service (DDoS) assaults in IoT networks. This research presents a comprehensive study on DDoS attack detection using the NSL-KDD dataset. The dataset contains a diverse set of network traffic data. This paper proposes two approaches, one utilizing Principal Component Analysis (PCA) and another without PCA, to compare their performance. Robust scaling and encoding techniques are applied as preprocessing steps. The experiment outcomes demonstrate a noteworthy improvement in the accuracy of DDoS attack detection in IoT devices by integrating PCA and Robust Scaler. Notably, the Random Forest and KNN classifiers demonstrate exceptional performance with an accuracy of 99.87 % and 99.14 %, respectively, while Naïve Bayes shows a lower accuracy of 87.14 %. The findings from this experiment contribute valuable insights into enhancing the security of IoT devices against DDoS attacks. The proposed approach showcases the importance of appropriate preprocessing techniques in achieving robust intrusion detection systems for IoT environments. |
| first_indexed | 2024-03-08T02:02:26Z |
| format | Article |
| id | doaj.art-186be42a8df34aba9566f34c8e2fd94b |
| institution | Directory Open Access Journal |
| issn | 1110-8665 |
| language | English |
| last_indexed | 2024-04-24T20:12:47Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj.art-186be42a8df34aba9566f34c8e2fd94b2024-03-23T06:23:21ZengElsevierEgyptian Informatics Journal1110-86652024-03-0125100450Enhancing DDoS attack detection in IoT using PCASanjit Kumar Dash0Sweta Dash1Satyajit Mahapatra2Sachi Nandan Mohanty3M. Ijaz Khan4Mohamed Medani5Sherzod Abdullaev6Manish Gupta7Odisha University of Technology and Research, Bhubaneswar, Odisha, IndiaOdisha University of Technology and Research, Bhubaneswar, Odisha, IndiaOdisha University of Technology and Research, Bhubaneswar, Odisha, IndiaSchool of Computer Science & Engineering (SCOPE), VIT-AP University, AP, IndiaDepartment of Mechanical Engineering, Lebanese American University, Beirut, Lebanon; Department of Mathematics and Statistics, Riphah International University I-14, Islamabad 44000, Pakistan; Corresponding author at: Department of Mechanical Engineering, Lebanese American University, Beirut, Lebanon.Department of Computer Science, College of Science and Art at Mahayil, King Khalid University, Muhayil Aseer 62529, Saudi ArabiaFaculty of Chemical Engineering, New Uzbekistan University, Tashkent, Uzbekistan; Department of Science and Innovation, Tashkent State Pedagogical University named after Nizami, Tashkent, UzbekistanDivision of Research and Technology, Lovely Professional University, Phagwara, IndiaInternet of Things (IoT) security and reliability rely on the capacity to identify distributed denial-of-service (DDoS) assaults in IoT networks. This research presents a comprehensive study on DDoS attack detection using the NSL-KDD dataset. The dataset contains a diverse set of network traffic data. This paper proposes two approaches, one utilizing Principal Component Analysis (PCA) and another without PCA, to compare their performance. Robust scaling and encoding techniques are applied as preprocessing steps. The experiment outcomes demonstrate a noteworthy improvement in the accuracy of DDoS attack detection in IoT devices by integrating PCA and Robust Scaler. Notably, the Random Forest and KNN classifiers demonstrate exceptional performance with an accuracy of 99.87 % and 99.14 %, respectively, while Naïve Bayes shows a lower accuracy of 87.14 %. The findings from this experiment contribute valuable insights into enhancing the security of IoT devices against DDoS attacks. The proposed approach showcases the importance of appropriate preprocessing techniques in achieving robust intrusion detection systems for IoT environments.http://www.sciencedirect.com/science/article/pii/S1110866524000136DDoS attackFeature SelectionPrincipal component analysisRandom forestKNNNaïve Bayes |
| spellingShingle | Sanjit Kumar Dash Sweta Dash Satyajit Mahapatra Sachi Nandan Mohanty M. Ijaz Khan Mohamed Medani Sherzod Abdullaev Manish Gupta Enhancing DDoS attack detection in IoT using PCA Egyptian Informatics Journal DDoS attack Feature Selection Principal component analysis Random forest KNN Naïve Bayes |
| title | Enhancing DDoS attack detection in IoT using PCA |
| title_full | Enhancing DDoS attack detection in IoT using PCA |
| title_fullStr | Enhancing DDoS attack detection in IoT using PCA |
| title_full_unstemmed | Enhancing DDoS attack detection in IoT using PCA |
| title_short | Enhancing DDoS attack detection in IoT using PCA |
| title_sort | enhancing ddos attack detection in iot using pca |
| topic | DDoS attack Feature Selection Principal component analysis Random forest KNN Naïve Bayes |
| url | http://www.sciencedirect.com/science/article/pii/S1110866524000136 |
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