A Deep Learning Approach for Malware and Software Piracy Threat Detection


  • K. Aldriwish Department of Computer Science, College of Science and Humanities, Majmaah University Majmaah, Saudi Arabia
Volume: 11 | Issue: 6 | Pages: 7757-7762 | December 2021 | https://doi.org/10.48084/etasr.4412


Internet of Things (IoT) -based systems need to be up to date on cybersecurity threats. The security of IoT networks is challenged by software piracy and malware attacks, and much important information can be stolen and used for cybercrimes. This paper attempts to improve IoT cybersecurity by proposing a combined model based on deep learning to detect malware and software piracy across the IoT network. The malware’s model is based on Deep Convolutional Neural Networks (DCNNs). Apart from this, TensorFlow Deep Neural Networks (TFDNNs) are introduced to detect software piracy threats according to source code plagiarism. The investigation is conducted on the Google Code Jam (GCJ) dataset. The conducted experiments prove that the classification performance achieves high accuracy of about 98%.


cybersecurity, malware, software piracy, deep learning, Internet of Things


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How to Cite

K. Aldriwish, “A Deep Learning Approach for Malware and Software Piracy Threat Detection”, Eng. Technol. Appl. Sci. Res., vol. 11, no. 6, pp. 7757–7762, Dec. 2021.


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