Development and Evaluation of Ensemble Learning Models for Detection of DDOS Attacks in IoT

Yıldıran YILMAZ; Selim BUYRUKOĞLU

doi:https://doi.org/10.17350/HJSE19030000257

Makale detayları

Development and Evaluation of Ensemble Learning Models for Detection of DDOS Attacks in IoT

Yazarlar:

Yıldıran YILMAZ | Selim BUYRUKOĞLU

Yayıncı:

Hitit University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Hittite Journal of Science and Engineering 2022 Vol.9 Issue 2, pp.73-82

Belge Tipi:

Makale

Yayın Tarihi:

6/2022

Belge Dili:

İngilizce

Document ID (Org No.):

2000451145

Sıklık:

Üç Aylık

eISSN:

2148-4171

DOI:

https://doi.org/10.17350/HJSE19030000257

Özet:

 

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.

Makale detayları

Development and Evaluation of Ensemble Learning Models for Detection of DDOS Attacks in IoT

Yazarlar: Yıldıran YILMAZ | Selim BUYRUKOĞLU

Yayıncı: Hitit University

Ana Başlıklar: Uygulamalı fizik

Dergi: Hittite Journal of Science and Engineering 2022 Vol.9 Issue 2, pp.73-82

Belge Tipi: Makale

Yayın Tarihi: 6/2022

Belge Dili: İngilizce

Document ID (Org No.): 2000451145

Sıklık: Üç Aylık

eISSN: 2148-4171

DOI: https://doi.org/10.17350/HJSE19030000257

Sayı

Yazarlar:

Yıldıran YILMAZ | Selim BUYRUKOĞLU

Yayıncı:

Hitit University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Hittite Journal of Science and Engineering 2022 Vol.9 Issue 2, pp.73-82

Belge Tipi:

Makale

Yayın Tarihi:

6/2022

Belge Dili:

İngilizce

Document ID (Org No.):

2000451145

Sıklık:

Üç Aylık

eISSN:

2148-4171

DOI:

https://doi.org/10.17350/HJSE19030000257