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primary topic: Botnet detection in internet of things using stacked ensemble learning model

Botnet detection in internet of things using stacked ensemble learning model
type: AcademicArticle
type: Article
abstract: Botnets are used for malicious activities such as cyber-attacks, spamming, and data theft and have become a significant threat to cyber security. Despite existing approaches for cyber attack detection, botnets prove to be a particularly difficult problem that calls for more advanced detection methods. In this research, a stacking classifier is proposed based on K-nearest neighbor, support vector machine, decision tree, random forest, and multilayer perceptron, called KSDRM, for botnet detection. Logistic regression acts as the meta-learner to combine the predictions from the base classifiers into the final prediction with the aim of increasing the overall accuracy and predictive performance of the ensemble. The UNSW-NB15 dataset is used to train machine learning models and evaluate their effectiveness in detecting cyber-attacks on IoT networks. The categorical features are transformed into numerical values using label encoding. Machine learning techniques are adopted to recognize botnet attacks to enhance cyber security measures. The KSDRM model successfully captures the complex patterns and traits of botnet attacks and obtains 99.99% training accuracy. The KSDRM model also performs well during testing by achieving an accuracy of 97.94%. Based on 3, 5, 7, and 10 folds, the k-fold cross-validation results show that the proposed method’s average accuracy is 99.89%, 99.88%, 99.89%, and 99.87%, respectively. Further, the demonstration of experiments and results shows the KSDRM model is an effective method to identify botnet-based cyber attacks. The findings of this study have the potential to improve cyber security controls and strengthen networks against changing threats.
date: 2025-07
volume: 15
issue: 1
is part of: Scientific Reports
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: Botnet detection in internet of things using stacked ensemble learning model (Texto)
has document: Botnet detection in internet of things using stacked ensemble learning model (Otro)
subject: Engineering

Botnet detection in internet of things using stacked ensemble learning model (Texto)
type: Document
has file: s41598-025-02008-9.pdf
has part: s41598-025-02008-9.pdf

Scientific Reports
type: Collection
issn: 20452322

Urooj Akram
type: Person
given name: Urooj
family name: Akram

Daniel Gavilanes Aray
type: Person
given name: Daniel
family name: Gavilanes Aray

Mudasir Ali
type: Person
given name: Mudasir
family name: Ali

Muhammad Faheem Mushtaq
type: Person
given name: Muhammad Faheem
family name: Mushtaq

Imran Ashraf
type: Person
given name: Imran
family name: Ashraf

Manuel Masías Vergara
type: Person
given name: Manuel
family name: Masías Vergara

Hanen Karamti
type: Person
given name: Hanen
family name: Karamti

Botnet detection in internet of things using stacked ensemble learning model (Otro)
type: Document
is version of: Botnet detection in internet of things using stacked ensemble learning model (Texto)
is volatile version of: Botnet detection in internet of things using stacked ensemble learning model (Texto)
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HTML Summary of #17838 Botnet detection in internet of things using stacked ensemble learning model
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primary topic: Botnet detection in internet of things using stacked ensemble learning model

Engineering
type: Concept
preferred label: Ingeniería @es