Using deep learning and zero-shot learning techniques to detect zero-day android malware
Publication Type
Conference Proceeding
Publication Date (Issue Year)
2023
Journal Name
International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023
Abstract
The prevalence of cyber security threats, such as the Android Zero-day vulnerability, is becoming increasingly worrisome. With the widespread use of Android-powered mobile devices, attackers are leveraging zero-day vulnerabilities to infect Android software at an alarming rate. Detecting zero-day vulnerabilities in Android applications is particularly challenging due to their unpatched and undiscovered nature, resulting in a lack of reference points for identification. In response to this issue, we propose a novel and effective system called Zero-Vuln, which is designed to classify and identify zero-day Android malware. Zero-Vuln leverages deep learning and zero-shot learning techniques, as well as established data-sets, to identify previously unknown malware. Our approach achieves a remarkable performance of 83% accuracy, as well as high precision and recall, and represents a significant contribution to the field of cyber security.
Keywords
Measurement, Mechatronics, Feature extraction, Malware, Computer crime, Smart phones
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., Dembélé, J. M., Mendy, G., & Ouya, S. (2023). Using deep learning and zero-shot learning techniques to detect zero-day android malware. International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023 https://doi.org/10.1109/ICECCME57830.2023.10252803