Title: IoT-based-malware-detection using artificial intelligence in the cyber security field

Authors: K.L.S.D.T. Keerthi Vardhan; V. Siva Ram Krishna Sarma

Addresses: Department of Computer Science and Engineering, Koneru Lakshmiah (KL) University, Vaddeswaram, Andhra Pradesh, India ' Department of Computer Science and Engineering, Koneru Lakshmiah (KL) University, Vaddeswaram, Andhra Pradesh, India

Abstract: The field of study for this work centres on enhancing security within the expanding domain of the internet of things (IoT), where the need for reliable detection of malicious activities is critical. As IoT integrates a wide array of applications and hardware, the inherent online nature of these technologies makes vital infrastructure susceptible to cyberattacks. Despite the involvement of a significant community in critical applications like CPSs, traditional computational methodologies in anomaly-based programs often prove insufficient. This study aims to identify and classify issues at both the network and host levels using advanced ML and DL models, which offer promising solutions. Specifically, the research employs the IoT-23 dataset to conduct a comprehensive analysis using algorithms such as DT, SVM, and ECLDNN. By evaluating the precision and energy efficiency of these classifiers, the study seeks to determine the most accurate and time-efficient solution for defect detection in IoT systems. This work advances the field by proposing and validating sophisticated ML and DL techniques that significantly improve the detection and classification of cyber threats, thereby enhancing the security of IoT infrastructure.

Keywords: decision trees; DT; enhanced convolutional long short-term memory deep neural network; ECLDNN; intrusion detection; IoT-23; machine learning; ML; malware; support vector machines; SVM; deep learning; DL; cyber-physical systems; CPSs.

DOI: 10.1504/IJCIS.2026.153815

International Journal of Critical Infrastructures, 2026 Vol.22 No.2, pp.157 - 181

Received: 15 Apr 2024
Accepted: 09 Jul 2024
Published online: 27 May 2026 *

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