Detecting Malware of Windows OS Using AI Classification for Image of Extracted Behavior Features

Malware detection is crucial for protecting digital environments. Traditional methods involve static and dynamic analysis, but recent advancements leverage artificial intelligence (AI) to enhance detection accuracy. This study aims to improve malware detection by integrating dynamic malware analysis with AI-driven techniques. The primary challenge addressed is accurately classifying and detecting malware based on behavior extracted from isolated virtual machines. By analyzing 50 malware samples and 11 benign programs, we extract ten behavioral features such as process ID, CPU usage, and network connections. We employ text-based classification using feedforward neural networks (FNN) and recurrent neural networks (RNN), achieving accuracy rates of 56% and 68%, respectively. Additionally, we convert the extracted features into grayscale images for image-based classification with a convolutional neural network (CNN), resulting in a higher accuracy of 70.1%. This multi-modal approach, combining behavioral analysis with AI, not only enhances detection accuracy but also provides a comprehensive understanding of malware behavior compared to competing methods.

@article{Dongshik2024,
  title     = {Detecting Malware of Windows OS Using AI Classification for Image of Extracted Behavior Features},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Kang Dongshik and Noor Aldeen Alhamedi},
  doi       = {10.14569/IJACSA.2024.0150949},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150949}
}