IOT-MDEDTL: IoT Malware Detection based on Ensemble Deep Transfer Learning

Majlesi Journal of Electrical Engineering

مقاله 7، دوره 16، شماره 3، آذر 2022، صفحه 47-54   

نوع مقاله: Reseach Article

شناسه دیجیتال (DOI): 10.30486/mjee.2022.696506

نویسندگان

Qasim Kadhim^{* 1}؛ Ahmed Qassem Ali Sharhan Al-Sudani²؛ Inas Amjed Almani³؛ Tawfeeq Alghazali⁴؛ Hasan Khalid Dabis⁵؛ Atheer Taha Mohammed⁶؛ Saad Ghazi Talib⁷؛ Rawnaq Adnan Mahmood⁸؛ Zahraa Tariq Sahi⁹؛ Yaqeen Mezaal¹⁰

¹English Language Department, Al-Mustaqbal University College, Babylon, Iraq

²Al-Manara College For Medical Sciences, Maysan, Iraq

³Department of Computer Technology Engineering, Al-Hadba University College, Iraq

⁴College of Media, Department of Journalism, The Islamic University in Najaf, Najaf, Iraq

⁵College of Islamic Science, Ahl Al Bayt University, Kerbala, Iraq

⁶The University of Mashreq, Iraq

⁷Law Department, Al-Mustaqbal University College, Babylon, Iraq

⁸Medical device engineering, Ashur University College, Baghdad, Iraq

⁹Department of Dentistry, Al-Zahrawi University College, Karbala, Iraq

¹⁰Al-Esraa University College, Baghdad, Iraq

چکیده

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.

کلیدواژه‌ها

Internet of Things؛ Deep Learning؛ Ensemble Learning؛ Transfer Learning؛ Convolutional Neural Networks؛ Malware Detection

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