IOT-MDEDTL: IoT Malware Detection based on Ensemble Deep Transfer Learning
The internet of Things (IoT) is a promising expansion of the traditional Internet, which provides the foundation for millions of devices to interact with each other. IoT enables these smart devices, such as home appliances, different types of vehicles, sensor controllers, and security cameras, to sh...
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| Format: | Article |
| Language: | English |
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OICC Press
2022-09-01
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| Series: | Majlesi Journal of Electrical Engineering |
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| Online Access: | https://oiccpress.com/mjee/article/view/4962 |
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| author | Qasim Kadhim Ahmed Qassem Ali Sharhan Al-Sudani Inas Amjed Almani Tawfeeq Alghazali Hasan Khalid Dabis Atheer Taha Mohammed Saad Ghazi Talib Rawnaq Adnan Mahmood Zahraa Tariq Sahi Yaqeen Mezaal |
| author_facet | Qasim Kadhim Ahmed Qassem Ali Sharhan Al-Sudani Inas Amjed Almani Tawfeeq Alghazali Hasan Khalid Dabis Atheer Taha Mohammed Saad Ghazi Talib Rawnaq Adnan Mahmood Zahraa Tariq Sahi Yaqeen Mezaal |
| author_sort | Qasim Kadhim |
| collection | DOAJ |
| description | The internet of Things (IoT) is a promising expansion of the traditional Internet, which provides the foundation for millions of devices to interact with each other. IoT enables these smart devices, such as home appliances, different types of vehicles, sensor controllers, and security cameras, to share information, and this has been successfully done to enhance the quality of user experience. IoT-based mediums in day-to-day life are, in fact, minuscule computational resources, which are adjusted to be thoroughly domain-specific. As a result, monitoring and detecting various attacks on these devices becomes feasible. As the statistics prove, in the Mirai and Brickerbot botnets, Distributed Denial-of-Service (DDoS) attacks have become increasingly ubiquitous. To ameliorate this, in this paper, we propose a novel approach for detecting IoT malware from the preprocessed binary data using transfer learning. Our method comprises two feature extractors, named ResNet101 and VGG16, which learn to classify input data as malicious and non-malicious. The input data is built from preprocessing and converting the binary format of data into gray-scale images. The feature maps obtained from these two models are fused together to further be classified. Extensive experiments exhibit the efficiency of the proposed approach in a well-known dataset, achieving the accuracy, precision, and recall of 96.31%, 95.31%, and 94.80%, respectively. |
| format | Article |
| id | doaj-art-aa6f18b6f36a4e0cadab920fec9d1dcb |
| institution | OA Journals |
| issn | 2345-377X 2345-3796 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | OICC Press |
| record_format | Article |
| series | Majlesi Journal of Electrical Engineering |
| spelling | doaj-art-aa6f18b6f36a4e0cadab920fec9d1dcb2025-08-20T01:47:45ZengOICC PressMajlesi Journal of Electrical Engineering2345-377X2345-37962022-09-0116310.30486/mjee.2022.696506IOT-MDEDTL: IoT Malware Detection based on Ensemble Deep Transfer LearningQasim Kadhim0Ahmed Qassem Ali Sharhan Al-Sudani1Inas Amjed Almani2Tawfeeq Alghazali3Hasan Khalid Dabis4Atheer Taha Mohammed5Saad Ghazi Talib6Rawnaq Adnan Mahmood7Zahraa Tariq Sahi8Yaqeen Mezaal9English Language Department, Al-Mustaqbal University College, Babylon, IraqAl-Manara College For Medical Sciences, Maysan, IraqDepartment of Computer Technology Engineering, Al-Hadba University College, IraqCollege of Media, Department of Journalism, The Islamic University in Najaf, Najaf, IraqCollege of Islamic Science, Ahl Al Bayt University, Kerbala, IraqThe University of Mashreq, IraqLaw Department, Al-Mustaqbal University College, Babylon, IraqMedical device engineering, Ashur University College, Baghdad, IraqDepartment of Dentistry, Al-Zahrawi University College, Karbala, IraqAl-Esraa University College, Baghdad, IraqThe internet of Things (IoT) is a promising expansion of the traditional Internet, which provides the foundation for millions of devices to interact with each other. IoT enables these smart devices, such as home appliances, different types of vehicles, sensor controllers, and security cameras, to share information, and this has been successfully done to enhance the quality of user experience. IoT-based mediums in day-to-day life are, in fact, minuscule computational resources, which are adjusted to be thoroughly domain-specific. As a result, monitoring and detecting various attacks on these devices becomes feasible. As the statistics prove, in the Mirai and Brickerbot botnets, Distributed Denial-of-Service (DDoS) attacks have become increasingly ubiquitous. To ameliorate this, in this paper, we propose a novel approach for detecting IoT malware from the preprocessed binary data using transfer learning. Our method comprises two feature extractors, named ResNet101 and VGG16, which learn to classify input data as malicious and non-malicious. The input data is built from preprocessing and converting the binary format of data into gray-scale images. The feature maps obtained from these two models are fused together to further be classified. Extensive experiments exhibit the efficiency of the proposed approach in a well-known dataset, achieving the accuracy, precision, and recall of 96.31%, 95.31%, and 94.80%, respectively.https://oiccpress.com/mjee/article/view/4962Convolutional neural networks. malware detectionDeep learningEnsemble LearningInternet of Thingstransfer learning |
| spellingShingle | Qasim Kadhim Ahmed Qassem Ali Sharhan Al-Sudani Inas Amjed Almani Tawfeeq Alghazali Hasan Khalid Dabis Atheer Taha Mohammed Saad Ghazi Talib Rawnaq Adnan Mahmood Zahraa Tariq Sahi Yaqeen Mezaal IOT-MDEDTL: IoT Malware Detection based on Ensemble Deep Transfer Learning Majlesi Journal of Electrical Engineering Convolutional neural networks. malware detection Deep learning Ensemble Learning Internet of Things transfer learning |
| title | IOT-MDEDTL: IoT Malware Detection based on Ensemble Deep Transfer Learning |
| title_full | IOT-MDEDTL: IoT Malware Detection based on Ensemble Deep Transfer Learning |
| title_fullStr | IOT-MDEDTL: IoT Malware Detection based on Ensemble Deep Transfer Learning |
| title_full_unstemmed | IOT-MDEDTL: IoT Malware Detection based on Ensemble Deep Transfer Learning |
| title_short | IOT-MDEDTL: IoT Malware Detection based on Ensemble Deep Transfer Learning |
| title_sort | iot mdedtl iot malware detection based on ensemble deep transfer learning |
| topic | Convolutional neural networks. malware detection Deep learning Ensemble Learning Internet of Things transfer learning |
| url | https://oiccpress.com/mjee/article/view/4962 |
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