Document Type : Reseach Article

Authors

• Qasim Kadhim ¹
• Ahmed Qassem Ali Sharhan Al-Sudani ²
• Inas Amjed Almani ³
• Tawfeeq Alghazali ⁴
• Hasan Khalid Dabis ⁵
• Atheer Taha Mohammed ⁶
• Saad Ghazi Talib ⁷
• Rawnaq Adnan Mahmood ⁸
• Zahraa Tariq Sahi ⁹
• Yaqeen Mezaal ¹⁰

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

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

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

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

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

⁶ The University of Mashreq, Iraq

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

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

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

¹⁰ Al-Esraa University College, Baghdad, Iraq

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.

Keywords

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