TY - GEN
T1 - Data mining of encrypted network traffic for adult content and gambling Android applications
AU - Wang, Luhua
AU - Ning, Zhengyao
AU - Cheng, Yanan
AU - Li, Yitao
AU - Zhang, Zhaoxin
N1 - Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
PY - 2024
Y1 - 2024
N2 - With the advancement of mobile computing technology, there has been a rampant increase in mobile-based pornography, gambling, and scams in the recent past, leading to deception and loss of personal property, resulting in serious societal and legal issues. Therefore, we decided to investigate adult and gambling applications, discover, and analyze their encrypted traffic characteristics. We crawled a massive database of malicious domains and automatically downloaded data, including 687 gambling applications and 334 adult Android applications. This paper presents an empirical research study based on the real traffic of mobile pornography and gambling applications, delving into data mining and analyzing their traffic characteristics at the packet and session flow levels. As these applications involve the dissemination of adult content and scams, running them on an emulator and collecting traffic files does not constitute a violation of privacy. Through quantitative analysis of the encrypted traffic features of such applications, this research aims to provide assistance in the identification and classification of adult and gambling applications and related tasks.
AB - With the advancement of mobile computing technology, there has been a rampant increase in mobile-based pornography, gambling, and scams in the recent past, leading to deception and loss of personal property, resulting in serious societal and legal issues. Therefore, we decided to investigate adult and gambling applications, discover, and analyze their encrypted traffic characteristics. We crawled a massive database of malicious domains and automatically downloaded data, including 687 gambling applications and 334 adult Android applications. This paper presents an empirical research study based on the real traffic of mobile pornography and gambling applications, delving into data mining and analyzing their traffic characteristics at the packet and session flow levels. As these applications involve the dissemination of adult content and scams, running them on an emulator and collecting traffic files does not constitute a violation of privacy. Through quantitative analysis of the encrypted traffic features of such applications, this research aims to provide assistance in the identification and classification of adult and gambling applications and related tasks.
KW - data mining
KW - data packets
KW - encrypted traffic flows
UR - https://www.scopus.com/pages/publications/85200558788
U2 - 10.1117/12.3031241
DO - 10.1117/12.3031241
M3 - 会议稿件
AN - SCOPUS:85200558788
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology, EIBDCT 2024
A2 - Zhang, Jie
A2 - Sun, Ning
PB - SPIE
T2 - 3rd International Conference on Electronic Information Engineering, Big Data, and Computer Technology, EIBDCT 2024
Y2 - 26 January 2024 through 28 January 2024
ER -