Improved Deep Maxout for IoT Botnet Attack Detection with LDA based Selected Feature Set

As the Internet of Things (IoT) grows in reputation and prevalence, concerns regarding its vulnerability to cyber-attacks persist. One significant security issue affecting IoT is the presence of botnets. While several studies have utilized machine learning (ML) techniques to detect botnets, existing...

Full description

Saved in:
Bibliographic Details
Published in2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) pp. 1 - 8
Main Authors Bojarajulu, Balaganesh, Tanwar, Sarvesh, Singh, Thipendra Pal
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.07.2023
Subjects
Artificial neural networks
Botnet
Computational modeling
DeepMaxout
Feature extraction
IoT
Machine learning
Measurement
Min-Max
Training
Online AccessGet full text

Cover

More Information
Summary:As the Internet of Things (IoT) grows in reputation and prevalence, concerns regarding its vulnerability to cyber-attacks persist. One significant security issue affecting IoT is the presence of botnets. While several studies have utilized machine learning (ML) techniques to detect botnets, existing approaches have limitations, particularly in terms of detection accuracy. This research paper proposes an improved approach for botnet attack detection by employing optimal features by utilizing the Improved DeepMaxout approach. This methodology enhances overall efficiency and accuracy in botnet detection. The findings of this study contribute to advancing the field of IoT security by addressing the limitations of previous research and providing a more practical approach to detecting botnet attacks.
ISSN:2473-7674
DOI:10.1109/ICCCNT56998.2023.10306651