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- Title
IOT-MDEDTL: IoT Malware Detection based on Ensemble Deep Transfer Learning.
- Authors
Kadhim, Q. Kh.; Al-Sudani, A. Q. A. Sharhan; Almani, I. Amjed; Alghazali, T.; Dabis, H. khalid; Mohammed, A. Taha; Talib, S. G.; Mahmood, R. A.; Sahi, Z. Tariq; Mezaal, Y. S.
- 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.
- Subjects
DEEP learning; BOTNETS; SMART devices; INTERNET of things; MALWARE; TELEVISION in security systems; CONVOLUTIONAL neural networks
- Publication
Majlesi Journal of Electrical Engineering, 2022, Vol 16, Issue 3, p47
- ISSN
2345-377X
- Publication type
Article
- DOI
10.52547/mjee.16.3.47