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Cross-Layer Distributed Attack Detection Model for the IoT

Cross-Layer Distributed Attack Detection Model for the IoT

Hassan I. Ahmed, Abdurrahman A. Nasr, Salah M. Abdel-Mageid, Heba K. Aslan
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 17
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781683180647|DOI: 10.4018/IJACI.300794
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MLA

Ahmed, Hassan I., et al. "Cross-Layer Distributed Attack Detection Model for the IoT." IJACI vol.13, no.1 2022: pp.1-17. http://doi.org/10.4018/IJACI.300794

APA

Ahmed, H. I., Nasr, A. A., Abdel-Mageid, S. M., & Aslan, H. K. (2022). Cross-Layer Distributed Attack Detection Model for the IoT. International Journal of Ambient Computing and Intelligence (IJACI), 13(1), 1-17. http://doi.org/10.4018/IJACI.300794

Chicago

Ahmed, Hassan I., et al. "Cross-Layer Distributed Attack Detection Model for the IoT," International Journal of Ambient Computing and Intelligence (IJACI) 13, no.1: 1-17. http://doi.org/10.4018/IJACI.300794

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Abstract

IoT is a huge network that contains many objects communicating with each other. It has a collection of sensitive data which is vulnerable to various threats at different layers. Due to the lack of infrastructure and the distributed control in IoT, there have been many security threats in all network layers. The security of IoT that is based on layered approaches has shortcomings such as the redundancy, inflexibility and inefficiently of security solutions. There are many harmful attacks in IoT network such as DoS and DDoS attacks which can compromise the IoT architecture in all layers. Consequently, cross layer approach is proposed as an effective and practical security defending mechanism. Cross-Layer Distributed Attack Detection model (CLDAD) is proposed to enhance security solution for IoT environment. CLDAD presents a general detection method of DDoS in sensing layer, network layer and application layer. CLDAD is based on big data analytics techniques which enable the detection process to be performed in distributed way, so the model can detect DDoS attacks in any layer on-the-fly and the model support the scalability of the IoT environment. CLDAD is tested based on three datasets, namely, artificial jamming attack dataset, BoT-IoT dataset, and BoT-IoT based HTTP. The results showed that the proposed model is efficient in detecting attacks in the three layers of the IoT and gives detection accuracy of 99.8% on average.

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