TY - GEN
T1 - Symbolic Prompt Tuning Completes the App Promotion Graph
AU - Ouyang, Zhongyu
AU - Zhang, Chunhui
AU - Hou, Shifu
AU - Ma, Shang
AU - Chen, Chaoran
AU - Li, Toby
AU - Xiao, Xusheng
AU - Zhang, Chuxu
AU - Ye, Yanfang
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Recent mobile applications (i.e., apps) have been extensively implanted with paid advertisements that promote other mobile apps, including malware that raises alarming concerns in cybersecurity. Excavating the app promotion patterns in the app-promoting ecosystem allows for early interceptions of malware installment, and hence has gained more attention in recent research. However, related data in the app-promoting ecosystem such as app developers and categories is often scarce, especially when the data is collected from a single data source. The scarce data is insufficient in training effective deep and complex models for app promotion pattern mining, and targeting the data scarcity problem is therefore the key to advancing research in app promotion pattern mining. Therefore, we aim to complete data in the app-promoting ecosystem to pave the way for app-promoting pattern mining. We present SymPrompt, a language model-based framework that leverages the symbolic prompts to complete the missing data in the app-promoting ecosystem. The symbolic prompts are tokens that provide extra contextual information that assists the model in completing the missing data. We devise two sets of symbolic prompts containing contextual information from the perspectives of data structure and data semantics to assist the model prediction. Through extensive experiments, we demonstrate SymPrompt ’s effectiveness in completing the missing in the app-promoting ecosystem. Code: https://github.com/zyouyang/SymPrompt
AB - Recent mobile applications (i.e., apps) have been extensively implanted with paid advertisements that promote other mobile apps, including malware that raises alarming concerns in cybersecurity. Excavating the app promotion patterns in the app-promoting ecosystem allows for early interceptions of malware installment, and hence has gained more attention in recent research. However, related data in the app-promoting ecosystem such as app developers and categories is often scarce, especially when the data is collected from a single data source. The scarce data is insufficient in training effective deep and complex models for app promotion pattern mining, and targeting the data scarcity problem is therefore the key to advancing research in app promotion pattern mining. Therefore, we aim to complete data in the app-promoting ecosystem to pave the way for app-promoting pattern mining. We present SymPrompt, a language model-based framework that leverages the symbolic prompts to complete the missing data in the app-promoting ecosystem. The symbolic prompts are tokens that provide extra contextual information that assists the model in completing the missing data. We devise two sets of symbolic prompts containing contextual information from the perspectives of data structure and data semantics to assist the model prediction. Through extensive experiments, we demonstrate SymPrompt ’s effectiveness in completing the missing in the app-promoting ecosystem. Code: https://github.com/zyouyang/SymPrompt
UR - http://www.scopus.com/inward/record.url?scp=85203881430&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85203881430&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-70381-2_12
DO - 10.1007/978-3-031-70381-2_12
M3 - Conference contribution
AN - SCOPUS:85203881430
SN - 9783031703805
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 183
EP - 198
BT - Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track - European Conference, ECML PKDD 2024, Proceedings
A2 - Bifet, Albert
A2 - Krilavičius, Tomas
A2 - Miliou, Ioanna
A2 - Nowaczyk, Slawomir
PB - Springer Science and Business Media Deutschland GmbH
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024
Y2 - 9 September 2024 through 13 September 2024
ER -