A Deep Learning Approach for Malware and Software Piracy Threat Detection
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 propos...
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| Format: | Article |
| Language: | English |
| Published: |
D. G. Pylarinos
2021-12-01
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| Series: | Engineering, Technology & Applied Science Research |
| Subjects: | |
| Online Access: | https://etasr.com/index.php/ETASR/article/view/4412 |
| _version_ | 1798028407112466432 |
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| author | K. Aldriwish |
| author_facet | K. Aldriwish |
| author_sort | K. Aldriwish |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-04-11T19:08:33Z |
| format | Article |
| id | doaj.art-984a2b80f9b2469cb03cdabf9fdceb9a |
| institution | Directory Open Access Journal |
| issn | 2241-4487 1792-8036 |
| language | English |
| last_indexed | 2024-04-11T19:08:33Z |
| publishDate | 2021-12-01 |
| publisher | D. G. Pylarinos |
| record_format | Article |
| series | Engineering, Technology & Applied Science Research |
| spelling | doaj.art-984a2b80f9b2469cb03cdabf9fdceb9a2022-12-22T04:07:41ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362021-12-0111610.48084/etasr.4412A Deep Learning Approach for Malware and Software Piracy Threat DetectionK. Aldriwish0Department of Computer Science, College of Science and Humanities, Majmaah University Majmaah, Saudi ArabiaInternet 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%.https://etasr.com/index.php/ETASR/article/view/4412cybersecuritymalwaresoftware piracydeep learningInternet of Things |
| spellingShingle | K. Aldriwish A Deep Learning Approach for Malware and Software Piracy Threat Detection Engineering, Technology & Applied Science Research cybersecurity malware software piracy deep learning Internet of Things |
| title | A Deep Learning Approach for Malware and Software Piracy Threat Detection |
| title_full | A Deep Learning Approach for Malware and Software Piracy Threat Detection |
| title_fullStr | A Deep Learning Approach for Malware and Software Piracy Threat Detection |
| title_full_unstemmed | A Deep Learning Approach for Malware and Software Piracy Threat Detection |
| title_short | A Deep Learning Approach for Malware and Software Piracy Threat Detection |
| title_sort | deep learning approach for malware and software piracy threat detection |
| topic | cybersecurity malware software piracy deep learning Internet of Things |
| url | https://etasr.com/index.php/ETASR/article/view/4412 |
| work_keys_str_mv | AT kaldriwish adeeplearningapproachformalwareandsoftwarepiracythreatdetection AT kaldriwish deeplearningapproachformalwareandsoftwarepiracythreatdetection |