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

  • Qasim Khlaif 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 S. Mezaal
Keywords: malware detection, convolutional neural networks, transfer learning, ensemble learning, deep learning

Abstract

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.

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Published
2022-07-13
How to Cite
Kadhim, Q., Al-Sudani, A. Q. A. S., Almani, I. A., Alghazali, T., Dabis, H. K., Mohammed, A. T., Talib, S. G., Mahmood, R. A., Sahi, Z. T., & Mezaal, Y. (2022). IOT-MDEDTL: IoT Malware Detection based on Ensemble Deep Transfer Learning. Majlesi Journal of Electrical Engineering, 16(3). Retrieved from http://mjee.iaumajlesi.ac.ir/index/index.php/ee/article/view/4781
Section
Articles

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