Cross-Layer Distributed Attack Detection Model for the IoT

Hassan I. Ahmed, Abdurrahman A. Nasr, Salah M. Abdel-Mageid and Heba K. Aslan
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Hassan I. Ahmed: Informatics Department, Electronics Research Institute, Cairo, Egypt
Abdurrahman A. Nasr: Computer Engineering, Faculty of Engineering, Al-Azhar University, Cairo, Egypt
Salah M. Abdel-Mageid: Computer Engineering Department, College of Computer Science and Engineering, Taibah University, Saudi Arabia & Computer Engineering, Faculty of Engineering, Al-Azhar University, Cairo, Egypt
Heba K. Aslan: Informatics Department, Electronics Research Institute, Cairo, Egypt & Center of Informatics Science, Faculty of Information Technology and Computer Science, Nile University, Egypt

International Journal of Ambient Computing and Intelligence (IJACI), 2022, vol. 13, issue 1, 1-17

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.

Date: 2022
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