Towards a DeepMalOb Improvement in the Use of Formal Security Risk Analysis Methods
Publication Type
Conference Proceeding
Publication Date (Issue Year)
2023
Journal Name
IEEE
Abstract
Researchers are concerned about the detection of obfuscated Android malware, and multiple studies have been proposed to address certain obfuscation techniques. However, the comprehensive consideration of all obfuscation techniques remains a critical cybersecurity challenge due to their mutations. To tackle this issue, we developed the DeepMalOb approach, which utilizes memory dumping and deep learning with MLP to detect obfuscated malicious applications. Although the approach has yielded satisfactory results, we acknowledge potential security risks associated with MLPs, such as adversarial attacks, model inversion attacks, overfitting, and model biases, which may impact the accuracy and robustness of the MLP model and render it vulnerable to obfuscated malware. To improve the DeepMalOb approach, we propose the use of formal security risk analysis methods with MLP to detect hidden malware in Android by analyzing the security risks associated with the MLP model and the input features used for training
Keywords
Training, Deep learning, Analytical models, Cloud computing, Focusing, Feature extraction, Malware
Rsif Scholar Name
Zakaria Sawadogo
Thematic Area
ICTs Including Big Data and Artificial Intelligence
Africa Host University (AHU)
University of Gaston Berger (UGB), Senegal
Recommended Citation
Sawadogo, Z., Khan, M. T., Dembelle, J. M., Mendy, G., & Ouya, S. (2023). Towards a DeepMalOb Improvement in the Use of Formal Security Risk Analysis Methods. IEEE, 1-5. https://doi.org/10.1109/cloudtech58737.2023.10366167