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. 2025 Jul 2;15(1):22537.
doi: 10.1038/s41598-025-06568-8.

Blockchain enhanced distributed denial of service detection in IoT using deep learning and evolutionary computation

V V S H Prasad  1 , Swathi Sowmya Bavirthi  2 , C S S Anupama  3 , E Laxmi Lydia  4 , K Sathesh Kumar  5 , Khalid Ammar  6 , Mohamad Khairi Ishak  7
Affiliations

Blockchain enhanced distributed denial of service detection in IoT using deep learning and evolutionary computation

V V S H Prasad et al. Sci Rep. .

Abstract

The Internet of Things (IoT) is emerging as a new trend mainly employed in developing numerous vital applications. These applications endure on a federal storage framework primarily concerned with multiple issues. Blockchain technology (BC) is one of the supportive methods for developing IoT-based applications. It is employed to solve the problems encountered in IoT applications. The attack Distributed Denial of Service (DDoS) is one of the leading security attacks in IoT systems. Attackers can effortlessly develop the exposures of IoT gadgets and restrain them as fragments of botnets to commence DDoS threats. The IoT devices are said to be resource-constrained with computing resources and restricted memory. As a developing technology, BC holds the possibility of resolving security problems in IoT. This paper proposes the Metaheuristic-Optimized Blockchain Framework for Attack Detection using a Deep Learning Model (MOBCF-ADDLM) method. The main intention of the MOBCF-ADDLM method is to deliver an effective method for detecting DDoS threats in an IoT environment using advanced techniques. The BC technology is initially applied to mitigate DDoS attacks by presenting decentralized security solutions. Furthermore, data preprocessing utilizes the min-max scaling method to convert input data into a beneficial format. Additionally, feature selection (FS) is performed using the Aquila optimizer (AO) technique to recognize the most relevant features from input data. The attack classification process employs the deep belief network (DBN) technique. Finally, the red panda optimizer (RPO) model modifies the hyper-parameter values of the DBN model optimally and results in higher classification performance. A wide range of experiments with the MOBCF-ADDLM approach is performed under the BoT-IoT Binary and Multiclass datasets. The performance validation of the MOBCF-ADDLM approach portrayed a superior accuracy value of 99.22% over existing models.

Keywords: Blockchain; DDoS attack; Deep learning; IoT; Red panda optimizer.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests. Ethics approval: This article contains no studies with human participants performed by any authors.

Figures

Fig. 1
Fig. 1
IoT networks-based DDoS attack detection.
Fig. 2
Fig. 2
Overall flow of MOBCF-ADDLM approach.
Fig. 3
Fig. 3
BC framework.
Fig. 4
Fig. 4
Steps involved in the AO technique.
Fig. 5
Fig. 5
Framework of DBN.
Fig. 6
Fig. 6
Framework of DBN.
Algorithm 1
Algorithm 1
RPO model.
Fig. 7
Fig. 7
BoT-IoT Binary dataset (a, b) confusion matrix and (c, d) PR and ROC curves.
Fig. 8
Fig. 8
Average of MOBCF-ADDLM model on BoT-IoT Binary dataset.
Fig. 9
Fig. 9
formula image curve of MOBCF-ADDLM model on BoT-IoT Binary dataset
Fig. 10
Fig. 10
Loss graph of MOBCF-ADDLM technique on BoT-IoT Binary dataset.
Fig. 11
Fig. 11
BoT-IoT Multiclass dataset (a, b) confusion matrices and (c, d) PR and ROC curve.
Fig. 12
Fig. 12
Average of MOBCF-ADDLM model on BoT-IoT Multiclass dataset.
Fig. 13
Fig. 13
formula image curve of MOBCF-ADDLM model on BoT-IoT Multiclass dataset
Fig. 14
Fig. 14
Loss graph of MOBCF-ADDLM model on BoT-IoT Multiclass dataset.
Fig. 15
Fig. 15
Comparative results of MOBCF-ADDLM technique with existing approaches.
Fig. 16
Fig. 16
PT outcome of MOBCF-ADDLM methodology with existing models.
See this image and copyright information in PMC

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