A Method by Utilizing Deep Learning to Identify Malware Within Numerous Industrial Sensors on IoTs

The industrial sensors of IoT is an emerging model, which combines Internet and the industrial physical smart objects. These objects belong to the broad domains like the smart homes, the smart cities, the processes of the industrial and the military, the agriculture and the business. Due to the substantial advancement in Industrial Internet of Things (IIoT) technologies, numerous IIoT applications have been developed over the past ten years. Recently, there have been multiple reports of malware-based cyber-attacks targeting IIoT systems. Consequently, this research focuses on creating an effective Artificial Intelligence (AI)-powered system for detecting zero-day malware in IIoT environments. In the current article, a combined framework for the detection of the malware basis on the deep learning (DL) is proposed, that uses the dual-density discrete wavelet transform for the extraction of the feature and a combination from the convolutional neural network (CNN) and the long-term short-term memory (LSTM). The method is utilized for malware detection and classification. It has been assessed using the Malimg dataset and the Microsoft BIG 2015 dataset. The results demonstrate that our proposed model can classify malware with remarkable accuracy, surpassing similar methods. When tested on the Microsoft BIG 2015 and Malimg datasets, the accuracy achieved is 95.36% and 98.12%, respectively.

@article{MA2024,
  title     = {A Method by Utilizing Deep Learning to Identify Malware Within Numerous Industrial Sensors on IoTs},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Ronghua MA},
  doi       = {10.14569/IJACSA.2024.0150822},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150822}
}