RT Journal Article
T1 Deep Learning-Based Attack Detection and Classification in Android Devices.
A1 Gómez Alfonso, Bernardo
A1 Muñoz-Gallego, Antonio Jesús
K1 Sistemas operativos - Protección
K1 Delitos informáticos
K1 Virus informáticos
AB The increasing proliferation of Androidbased devices, which currently dominate themarket with a staggering 72% global market share, has made them a prime target for attackers.Consequently, the detection of Android malware has emerged as a critical research area. Bothacademia and industry have explored various approaches to develop robust and efficient solutionsfor Android malware detection and classification, yet it remains an ongoing challenge. In this study,we present a supervised learning technique that demonstrates promising results in Android malwaredetection. The key to our approach lies in the creation of a comprehensive labeled dataset, comprisingover 18,000 samples classified into five distinct categories: Adware, Banking, SMS, Riskware, andBenign applications. The effectiveness of our proposed model is validated using well-establisheddatasets such as CICMalDroid2020, CICMalDroid2017, and CICAndMal2017. Comparing our resultswith state-of-the-art techniques in terms of precision, recall, efficiency, and other relevant factors,our approach outperforms other semi-supervised methods in specific parameters. However, weacknowledge that our model does not exhibit significant deviations when compared to alternativeapproaches concerning certain aspects. Overall, our research contributes to the ongoing efforts in thedevelopment of advanced techniques for Android malware detection and classification. We believethat our findings will inspire further investigations, leading to enhanced security measures andprotection for Android devices in the face of evolving threats.
PB MDPI
YR 2023
FD 2023-07-28
LK https://hdl.handle.net/10630/31618
UL https://hdl.handle.net/10630/31618
LA eng
NO Gómez, A., & Muñoz, A. (2023). Deep Learning-Based Attack Detection and Classification in Android Devices. Electronics, 12(15), 3253. https://doi.org/10.3390/electronics12153253
NO Partial funding for open access charge: Universidad de Málaga
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026