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DADEM: Distributed Attack Detection Model Based on Big Data Analytics for the Enhancement of the Security of Internet of Things (IoT)

DADEM: Distributed Attack Detection Model Based on Big Data Analytics for the Enhancement of the Security of Internet of Things (IoT)

Hassan I. Ahmed, Abdurrahman A. Nasr, Salah M. Abdel-Mageid, Heba K. Aslan
Copyright: © 2021 |Volume: 12 |Issue: 1 |Pages: 26
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781799860266|DOI: 10.4018/IJACI.2021010105
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MLA

Ahmed, Hassan I., et al. "DADEM: Distributed Attack Detection Model Based on Big Data Analytics for the Enhancement of the Security of Internet of Things (IoT)." IJACI vol.12, no.1 2021: pp.114-139. http://doi.org/10.4018/IJACI.2021010105

APA

Ahmed, H. I., Nasr, A. A., Abdel-Mageid, S. M., & Aslan, H. K. (2021). DADEM: Distributed Attack Detection Model Based on Big Data Analytics for the Enhancement of the Security of Internet of Things (IoT). International Journal of Ambient Computing and Intelligence (IJACI), 12(1), 114-139. http://doi.org/10.4018/IJACI.2021010105

Chicago

Ahmed, Hassan I., et al. "DADEM: Distributed Attack Detection Model Based on Big Data Analytics for the Enhancement of the Security of Internet of Things (IoT)," International Journal of Ambient Computing and Intelligence (IJACI) 12, no.1: 114-139. http://doi.org/10.4018/IJACI.2021010105

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Abstract

Nowadays, Internet of Things (IoT) is considered as part our lives and it includes different aspects - from wearable devices to smart devices used in military applications. IoT connects a variety of devices and as such, the generated data is considered as ‘Big Data'. There has however been an increase in attacks in this era of IoT since IoT carries crucial information regarding banking, environmental, geographical, medical, and other aspects of the daily lives of humans. In this paper, a Distributed Attack Detection Model (DADEM) that combines two techniques - Deep Learning and Big Data analytics - is proposed. Sequential Deep Learning model is chosen as a classification engine for the distributed processing model after testing its classification accuracy against other classification algorithms like logistic regression, KNN, ID3 decision tree, CART, and SVM. Results showed that Sequential Deep Learning model outperforms the aforementioned ones. The classification accuracy of DADEM approaches 99.64% and 99.98% for the UNSW-NB15 and BoT-IoT datasets, respectively. Moreover, a plan is proposed for optimizing the proposed model to reduce the overhead of the overall system operation in a constrained environment like IoT.

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