TY - GEN
T1 - MAPS:
T2 - 17th ACM Workshop on Mobility in the Evolving Internet Architecture, MobiArch 2022
AU - Pasdar, Amirmohammad
AU - Lee, Young Choon
AU - Hong, Seok-hee
AU - Liu, Tongliang
PY - 2022/10
Y1 - 2022/10
N2 - The vulnerability of smartphones to cyber attacks has been a serious concern for users emerging from the integrity of installed mobile applications (apps). These applications are fundamentally developed to provide legitimate and diversified on-the-go services; however, harmful and dangerous ones have found the perfect door to get into smartphones performing malicious behaviors. More effective and sophisticated application analysis is a practical strategy to reveal malicious activities and provide more insights into the application behavior. In this paper, we present the Malware Analysis and Profiling on Smartphones (MAPS) dataset for the research community that provides a very in-depth and detailed deep analysis of applications for extracting an enhanced set of features. The MAPS dataset relies on a diverse dataset collected from different repositories, containing more than 153000 applications (>2TB in size), and outputs more than 40000 features for analysis and training of deep neural network models.
AB - The vulnerability of smartphones to cyber attacks has been a serious concern for users emerging from the integrity of installed mobile applications (apps). These applications are fundamentally developed to provide legitimate and diversified on-the-go services; however, harmful and dangerous ones have found the perfect door to get into smartphones performing malicious behaviors. More effective and sophisticated application analysis is a practical strategy to reveal malicious activities and provide more insights into the application behavior. In this paper, we present the Malware Analysis and Profiling on Smartphones (MAPS) dataset for the research community that provides a very in-depth and detailed deep analysis of applications for extracting an enhanced set of features. The MAPS dataset relies on a diverse dataset collected from different repositories, containing more than 153000 applications (>2TB in size), and outputs more than 40000 features for analysis and training of deep neural network models.
KW - Android malware analysis
KW - Android malware dataset
KW - malware profiling
UR - https://www.scopus.com/pages/publications/85141054231
U2 - 10.1145/3556548.3559629
DO - 10.1145/3556548.3559629
M3 - Conference proceeding contribution
AN - SCOPUS:85141054231
T3 - Proceedings of the 17th ACM Workshop on Mobility in the Evolving Internet Architecture, MobiArch 2022
SP - 13
EP - 18
BT - Proceedings of the 17th ACM Workshop on Mobility in the Evolving Internet Architecture, MobiArch 2022
PB - Association for Computing Machinery, Inc
CY - New York, NY
Y2 - 21 October 2022 through 21 October 2022
ER -