Detecting DDoS Attacks in IoT Environment

Yasmine Labiod, Abdelaziz Amara Korba and Nacira Ghoualmi-Zine
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Yasmine Labiod: Networks and Systems Laboratory, Badji Mokhtar Annaba University, Algeria
Abdelaziz Amara Korba: Networks and Systems Laboratory, Badji Mokhtar Annaba University, Algeria
Nacira Ghoualmi-Zine: Networks and Systems Laboratory, Badji Mokhtar Annaba University, Algeria

International Journal of Information Security and Privacy (IJISP), 2021, vol. 15, issue 2, 145-180

Abstract: With the great potential of internet of things (IoT) infrastructure in different domains, cyber-attacks are also rising commensurately. Distributed denials of service (DDoS) attacks are one of the cyber security threats. This paper will focus on DDoS attacks by adding the design of an intrusion detection system (IDS) tailored to IoT systems. Moreover, machine learning techniques will be investigated to distinguish the data representing flows of network traffic, which include both normal and DDoS traffic. In addition, these techniques will be used to help make a refined detection model for identifying different types of DDoS attacks. Furthermore, the performance of machine learning-based proposed solution is validated using N-BaIoT dataset and compared through different evaluation metrics. The experimental results show that the proposed IDS not only detects DDoS attacks types but also has a high detection rate and low false positive rate, which argues the usefulness of the proposed approach in comparison with several existing DDoS attacks detection techniques.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jisp00:v:15:y:2021:i:2:p:145-180

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