HHR Manzil, S Manohar Naik - Cybersecurity, 2023 - Springer
Malware attacks on the Android platform are rapidly increasing due to the high consumer adoption of Android smartphones. Advanced technologies have motivated cyber-criminals to …
This paper introduces and tests a novel machine learning approach to detect Android malware. The proposed approach is composed of Support Vector Machine (SVM) classifier …
Android smartphones are being utilized by a vast majority of users for everyday planning, data exchanges, correspondences, social interaction, business execution, bank …
Mobile device manufacturers are rapidly producing miscellaneous Android versions worldwide. Simultaneously, cyber criminals are executing malicious actions, such as …
Android OS is one of the widely used mobile Operating Systems. The number of malicious applications and adwares are increasing constantly on par with the number of mobile …
P Agrawal, B Trivedi - … , Analytics and Innovation: Proceedings of ICDMAI …, 2021 - Springer
With the growing popularity of Android devices, it is also more prone to malware attacks. There are many malware scanning tools available for scanning the Android Malware but …
E Odat, QM Yaseen - IEEE Access, 2023 - ieeexplore.ieee.org
This paper proposes a machine learning model based on the co-existence of static features for Android malware detection. The proposed model assumes that Android malware …
J Lee, H Jang, S Ha, Y Yoon - Mathematics, 2021 - mdpi.com
Since the discovery that machine learning can be used to effectively detect Android malware, many studies on machine learning-based malware detection techniques have …
K Liu, S Xu, G Xu, M Zhang, D Sun, H Liu - IEEE access, 2020 - ieeexplore.ieee.org
Android applications are developing rapidly across the mobile ecosystem, but Android malware is also emerging in an endless stream. Many researchers have studied the …