INTER
发表于 2025-3-25 06:06:07
https://doi.org/10.1007/978-3-540-76376-5ses. The state-of-the-art solutions, such as Chen et al., (Stormdroid: A streaminglized machine learning-based system for detecting android malware, in . (2016), pp. 377–388), Kharraz et al. (UNVEIL: A large-scale, automated approach to detecting ransomware, in . (2016), pp. 757–772) and Sgandurra e
闪光东本
发表于 2025-3-25 07:37:37
http://reply.papertrans.cn/16/1571/157072/157072_22.png
稀释前
发表于 2025-3-25 14:37:13
https://doi.org/10.1007/978-3-540-76376-5focuses on portable malware detection based on applying supervised machine learning on static analysis features in contrast to ., presented in Chap. ., in which we propose an unsupervised system based on static analysis features. While . provides a framework for malware clustering, aimed at market l
上下连贯
发表于 2025-3-25 19:03:39
https://doi.org/10.1007/978-3-540-76376-5chniques by introducing code randomization during training; (2) adaptation to operating system and malware change overtime by introducing the use of confidence-based decisions to collect adaptation datasets overtime. In this context, we identify several limitations and gaps in the state-of-the-art A
相信
发表于 2025-3-25 23:49:12
https://doi.org/10.1007/978-3-540-76376-5running on smart devices are nowadays ubiquitous due to their convenience. For instance, users can presently use apps as Google Pay service to purchase products online and to make payments in retail stores. However, the growth of the mobile market apps has increased the concerns about the security o
Palate
发表于 2025-3-26 03:15:41
https://doi.org/10.1007/978-3-030-74664-3Android; malware detection; Fingerprinting; mobile security; cybersecurity; machine Learning
Coronary-Spasm
发表于 2025-3-26 05:33:34
978-3-030-74666-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
Osteons
发表于 2025-3-26 10:00:26
Resilient and Adaptive Android Malware Fingerprinting and Detection,chniques by introducing code randomization during training; (2) adaptation to operating system and malware change overtime by introducing the use of confidence-based decisions to collect adaptation datasets overtime. In this context, we identify several limitations and gaps in the state-of-the-art Android malware detection solutions.
Maximize
发表于 2025-3-26 13:40:37
http://reply.papertrans.cn/16/1571/157072/157072_29.png
HALO
发表于 2025-3-26 17:06:16
http://reply.papertrans.cn/16/1571/157072/157072_30.png