Development and Evaluation of Ensemble Learning Models for Detection of DDOS Attacks in IoT
Internet of Things that process tremendous confidential data have difficulty performing traditional security algorithms, thus their security is at risk. The security tasks to be added to these devices should be able to operate without disturbing the smooth operation of the system so that the availab...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Hitit University
2022-06-01
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| Series: | Hittite Journal of Science and Engineering |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/1929198 |
| _version_ | 1827796150600597504 |
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| author | Selim Buyrukoğlu Yıldıran Yılmaz |
| author_facet | Selim Buyrukoğlu Yıldıran Yılmaz |
| author_sort | Selim Buyrukoğlu |
| collection | DOAJ |
| description | Internet of Things that process tremendous confidential data have difficulty performing traditional security algorithms, thus their security is at risk. The security tasks to be added to these devices should be able to operate without disturbing the smooth operation of the system so that the availability of the system will not be impaired. While various attack detection systems can detect attacks with high accuracy rates, it is often impos-sible to integrate them into Internet of Things devices. Therefore, in this work, the new Distributed Denial-of-Service (DDoS) detection models using feature selection and learn-ing algorithms jointly are proposed to detect DDoS attacks, which are the most common type encountered by Internet of Things networks. Additionally, this study evaluates the memory consumption of single-based, bagging, and boosting algorithms on the client-side which has scarce resources. Not only the evaluation of memory consumption but also development of ensemble learning models refer to the novel part of this study. The data set consisting of 79 features in total created for the detection of DDoS attacks was minimized by selecting the two most significant features. Evaluation results confirm that the DDoS attack can be detected with high accuracy and less memory usage by the base models com-pared to complex learning methods such as bagging and boosting models. As a result, the findings demonstrate the feasibility of the base models, for the Internet of Things DDoS detection task, due to their application performance. |
| first_indexed | 2024-03-11T19:03:19Z |
| format | Article |
| id | doaj.art-617f8ed7e46e4bc29c92a62eae9ad6b9 |
| institution | Directory Open Access Journal |
| issn | 2148-4171 |
| language | English |
| last_indexed | 2024-03-11T19:03:19Z |
| publishDate | 2022-06-01 |
| publisher | Hitit University |
| record_format | Article |
| series | Hittite Journal of Science and Engineering |
| spelling | doaj.art-617f8ed7e46e4bc29c92a62eae9ad6b92023-10-10T11:17:29ZengHitit UniversityHittite Journal of Science and Engineering2148-41712022-06-0192738210.17350/HJSE19030000257150Development and Evaluation of Ensemble Learning Models for Detection of DDOS Attacks in IoTSelim Buyrukoğlu0Yıldıran Yılmaz1CANKIRI KARATEKIN UNIVERSITYRECEP TAYYIP ERDOGAN UNIVERSITYInternet of Things that process tremendous confidential data have difficulty performing traditional security algorithms, thus their security is at risk. The security tasks to be added to these devices should be able to operate without disturbing the smooth operation of the system so that the availability of the system will not be impaired. While various attack detection systems can detect attacks with high accuracy rates, it is often impos-sible to integrate them into Internet of Things devices. Therefore, in this work, the new Distributed Denial-of-Service (DDoS) detection models using feature selection and learn-ing algorithms jointly are proposed to detect DDoS attacks, which are the most common type encountered by Internet of Things networks. Additionally, this study evaluates the memory consumption of single-based, bagging, and boosting algorithms on the client-side which has scarce resources. Not only the evaluation of memory consumption but also development of ensemble learning models refer to the novel part of this study. The data set consisting of 79 features in total created for the detection of DDoS attacks was minimized by selecting the two most significant features. Evaluation results confirm that the DDoS attack can be detected with high accuracy and less memory usage by the base models com-pared to complex learning methods such as bagging and boosting models. As a result, the findings demonstrate the feasibility of the base models, for the Internet of Things DDoS detection task, due to their application performance.https://dergipark.org.tr/tr/download/article-file/1929198attack detectionbaggingbaseboostingddos |
| spellingShingle | Selim Buyrukoğlu Yıldıran Yılmaz Development and Evaluation of Ensemble Learning Models for Detection of DDOS Attacks in IoT Hittite Journal of Science and Engineering attack detection bagging base boosting ddos |
| title | Development and Evaluation of Ensemble Learning Models for Detection of DDOS Attacks in IoT |
| title_full | Development and Evaluation of Ensemble Learning Models for Detection of DDOS Attacks in IoT |
| title_fullStr | Development and Evaluation of Ensemble Learning Models for Detection of DDOS Attacks in IoT |
| title_full_unstemmed | Development and Evaluation of Ensemble Learning Models for Detection of DDOS Attacks in IoT |
| title_short | Development and Evaluation of Ensemble Learning Models for Detection of DDOS Attacks in IoT |
| title_sort | development and evaluation of ensemble learning models for detection of ddos attacks in iot |
| topic | attack detection bagging base boosting ddos |
| url | https://dergipark.org.tr/tr/download/article-file/1929198 |
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