Original scientific paper
https://doi.org/10.1080/00051144.2023.2288486
Botnet detection in the internet-of-things networks using convolutional neural network with pelican optimization algorithm
Swapna Thota
; Department of Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, India
*
D. Menaka
; Department of Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, India
* Corresponding author.
Abstract
Hackers nowadays employ botnets to undertake cyberattacks towards the Internet of Things
(IoT) by illegally exploiting the scattered network’s resources of computing devices. Several
Machine Learning (ML) and Deep Learning (DL) methods for detecting botnet (BN) assaults in
IoT networks have recently been proposed. However, in the training set, severely imbalanced
network traffic data degrades the classification performances of state-of-the-art ML as well as
DL algorithm, particularly in classes with very few samples. The Convolutional Neural Network
-Pelican Optimization System (CNN-POA) is a DL relied botnet attack detection algorithm developed in this research. Meanwhile, typical evaluation markers are used to compare the overall
performance of the proposed CNN-POA and additional frequently employed algorithms. The
simulation results suggest that the CNN-POA method is effective and dependable for detecting IoT network intrusion attacks. Experiments revealed that the suggested CNN-POA approach
outperformed a number of current metaheuristic algorithms, with an accuracy of 99.5%.
Keywords
Botnet detection; internet of things; optimization; convolutional neural networks
Hrčak ID:
322969
URI
Publication date:
27.12.2023.
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