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Blockchain Assisted Intrusion Detection System Using Differential Flower Pollination Model

Mohammed Altaf Ahmed1, Sara A Althubiti2, Dronamraju Nageswara Rao3, E. Laxmi Lydia4, Woong Cho5, Gyanendra Prasad Joshi6, Sung Won Kim7,*

1 Department of Computer Engineering, College of Computer Engineering & Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
2 Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, 11952, Saudi Arabia
3 Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, 140401, India
4 Department of Computer Science and Engineering, Vignan’s Institute of Information Technology, Visakhapatnam, 530049, India
5 Department of Software Convergence, Daegu Catholic University, Gyeongsan, 38430, Korea
6 Department of Computer Science and Engineering, Sejong University, Seoul, 05006, Korea
7 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, Gyeongbuk-do, 38541, Korea

* Corresponding Author: Sung Won Kim. Email:

Computers, Materials & Continua 2022, 73(3), 4695-4711.


Cyberattacks are developing gradually sophisticated, requiring effective intrusion detection systems (IDSs) for monitoring computer resources and creating reports on anomalous or suspicious actions. With the popularity of Internet of Things (IoT) technology, the security of IoT networks is developing a vital problem. Because of the huge number and varied kinds of IoT devices, it can be challenging task for protecting the IoT framework utilizing a typical IDS. The typical IDSs have their restrictions once executed to IoT networks because of resource constraints and complexity. Therefore, this paper presents a new Blockchain Assisted Intrusion Detection System using Differential Flower Pollination with Deep Learning (BAIDS-DFPDL) model in IoT Environment. The presented BAIDS-DFPDL model mainly focuses on the identification and classification of intrusions in the IoT environment. To accomplish this, the presented BAIDS-DFPDL model follows blockchain (BC) technology for effective and secure data transmission among the agents. Besides, the presented BAIDS-DFPDL model designs Differential Flower Pollination based feature selection (DFPFS) technique to elect features. Finally, sailfish optimization (SFO) with Restricted Boltzmann Machine (RBM) model is applied for effectual recognition of intrusions. The simulation results on benchmark dataset exhibit the enhanced performance of the BAIDS-DFPDL model over other models on the recognition of intrusions.


Cite This Article

M. A. Ahmed, S. A. Althubiti, D. Nageswara Rao, E. L. Lydia, W. Cho et al., "Blockchain assisted intrusion detection system using differential flower pollination model," Computers, Materials & Continua, vol. 73, no.3, pp. 4695–4711, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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