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All Journal Brilliance: Research of Artificial Intelligence
Nashrullah, Naufal
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Decision Sciences, Operations Research & Management Mathematics Other

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Detection of Malware Threats in Internet of Things Using Deep Learning Nashrullah, Naufal; Wahyu, Ari Purno
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.3869

Abstract

This paper examines the potential risks associated with the Internet of Things (IoT) as a new gateway for cyberattacks. The continuous access it provides to systems, applications, and services within organizations increases the likelihood of serious threats, such as software piracy and malware attacks, which can result in the theft of sensitive information and significant economic losses. To address these concerns, researchers have proposed the use of Deep Convolutional Neural Network (DCNN) to detect malware infections in IoT networks by analyzing color image visualization. The malware samples were obtained from the Android Malware dataset on Kaggle. The proposed deep learning method, namely the Deep Convolutional Neural Network, was employed to detect malware infections in IoT networks.
Detection of Malware Threats in Internet of Things Using Deep Learning Nashrullah, Naufal; Wahyu, Ari Purno
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.3869

Abstract

This paper examines the potential risks associated with the Internet of Things (IoT) as a new gateway for cyberattacks. The continuous access it provides to systems, applications, and services within organizations increases the likelihood of serious threats, such as software piracy and malware attacks, which can result in the theft of sensitive information and significant economic losses. To address these concerns, researchers have proposed the use of Deep Convolutional Neural Network (DCNN) to detect malware infections in IoT networks by analyzing color image visualization. The malware samples were obtained from the Android Malware dataset on Kaggle. The proposed deep learning method, namely the Deep Convolutional Neural Network, was employed to detect malware infections in IoT networks.
Co-Authors Ari Purno Wahyu Wibowo