Abstract

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%.

Highlights

  • Artificial intelligence (AI) approaches are overgrowing through machine learning and deep learning technologies

  • Providing secure Internet of Things (IoT) networks is the purpose of many malware detection and intelligent software plagiarism techniques

  • A Deep Learning approach based on the TensorFlow Deep Neural Network is proposed to detect software piracy through source code plagiarism

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Summary

A Deep Learning Approach for Malware and Software Piracy Threat Detection

Abstract-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%

INTRODUCTION
Software Piracy Detection
Malware Detection
THE PROPOSED ARCHITECTURE
Software Piracy Threat Detection Model
Malware Threat Detection Model
Software Piracy Detection Performance Evaluation
CONCLUSION
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